first commit

LICENSE

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+MIT License
+
+Copyright (c) 2022 LIYu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# Topology-Optimization-in-Julia
+
+Julia Codes for Structural Topology Optimization Design
+
+## Codes
+
+A personal [Julia](https://epubs.siam.org/doi/10.1137/141000671) code is given mainly based on a compact and efficient Matlab implementation **top99neo** of compliance topology optimization (TO) for 2D continua[^1], which is a v3.0 version of the celebrated **top99** Matlab code developed by Sigmund[^2] and **top88** by its heir[^3].
+
+Assemble just one half of the sysmetric stiffness matrix, thus substantial speedups are acheived.
+
+`top_oc/` and `top_mma/` contain corresponding files related to the TO with OC and MMA algorithms, respectively.
+
+Running codes could be tried out as:
+
+```julia
+include("./top99neo_mma.jl")
+setup = SetUp() # problem setup
+mat = Mat() # material property
+disfeature = DiscretizationFeature(setup, mat) # model discretization
+load = LoadsSupportsBCs(setup, disfeature) # boudary conditions
+ini = Initialization(setup, disfeature, mat) # initial conditions
+filter = Filter(setup) # filtering
+xPhys, opt_hist, vf_hist, anim = Optimization(setup, mat, load, filter, ini, disfeature) # optimization process
+gif(anim, "./res/des_hist.gif", fps=20) # design result visulization
+```
+
+## Results
+
+A benchmark MBB example is presented. TO design results are saved in `./res/` folder and a evolution history is shown as below.
+
+<!-- ![TO design evolution with MMA](./top_mma/res/des_hist.gif) -->
+
+<div align=center>
+<img src=./top_mma/res/des_hist.gif width="500">
+👍 💯
+</div>
+
+## Packages
+
+Run [the Julia REPL](https://docs.julialang.org/en/v1/stdlib/REPL/), enter `]` to bring up Julia's [package manager](https://docs.julialang.org/en/v1/stdlib/Pkg/),
+and add the listed packages:
+
+> julia> ]
+>  
+> (@v1.7) pkg> add #pkg_name#
+
+- Scientific computing
+  - LinearAlgebra
+  - SparseArrays
+  - Statistics : mean
+- Image process
+  - ImageFiltering: imfilter
+- Modelling
+  - [Gmsh](https://onlinelibrary.wiley.com/doi/10.1002/nme.2579)
+- FEM
+  - [Gridap](https://joss.theoj.org/papers/10.21105/joss.02520)
+- AD
+  - [ForwardDiff](https://arxiv.org/abs/1607.07892)
+  - [Zygote](https://arxiv.org/abs/1810.07951)
+- Optimization
+  - [NLopt](https://github.com/stevengj/nlopt)
+- Visualization
+  - Plots
+
+## TODOs
+
+- [x] [`top99neo.jl`](./top_oc/top99neo.jl)
+  top99neo.m rewritten in Julia
+- [x] [`MMA.jl`](./top_mma/MMA.jl)
+  MMA algorithm (mmasub.m + subsolve.m) rewritten in Julia
+- [x] [`top99neo_mma.jl`](./top_mma/top99neo_mma.jl)
+  2D code (top99neo + MMA) written in Julia
+- [ ] `top99neo_AD.jl`
+  Sensitivity Analysis using Automatic Differentiation
+- [ ] `top99neo_NLopt.jl`
+  Optimization solved with NLopt
+- [ ] `top3D.jl`
+  3D code (top3D125 + MMA) written in Julia
+- [ ] `top_flux.jl`
+  Combine TO with machine learning through [Flux](https://arxiv.org/abs/1811.01457)
+
+## Acknowledgements
+
+- [TopOpt Group](https://www.topopt.mek.dtu.dk/) 🇩🇰
+Matlab codes for topology optimization
+
+  - v1.0 [**top99.m**](https://www.topopt.mek.dtu.dk/Apps-and-software/A-99-line-topology-optimization-code-written-in-MATLAB)
+    Educatianal TO enlightenment for every beginners
+  - v2.0 [**top88.m**](https://www.topopt.mek.dtu.dk/Apps-and-software/Efficient-topology-optimization-in-MATLAB)
+    Loop vectorization and memory preallocation
+  - v3.0 [**top99neo.m**](https://www.topopt.mek.dtu.dk/Apps-and-software/New-99-line-topology-optimization-code-written-in-MATLAB)
+   Half matrix assembly operation, filter implementation and volume-preserving density projection
+
+- Prof. [Krister Svanberg](https://people.kth.se/~krille/) 🇸🇪
+  - Freely available [Matlab code](http://www.smoptit.se/) for CCSA/MMA[^4] and GCMMA
+
+## Author ©️
+
+📧 Please contact to liyu_npu@outlook.com
+
+⚠️ Disclaimer: The author reserves all rights but does not guarantee that the code is free from errors. Furthermore, we shall not be liable in any event.
+
+| Name  |   Info.    |     Hobby     |   Food    |
+| ----- | :--------: | :-----------: | :-------: |
+| Yu Li | 🇨🇳 🎓 1️⃣9️⃣9️⃣0️⃣ ♑ | 🎧 🃏 🎮 🏀 🏊 🏃 🚴‍♂️ | 🍦 🦞 🍣 🌽 🍌 |
+
+```bibtex
+@misc{Yu2022,
+  author = {Yu Li},
+  title = {Topology Optimization in Julia},
+  year = {2022},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/yuloveyet/Topology-Optimization-in-Julia}},
+  }
+```
+
+**References**
+[^1]: Ferrari, F., & Sigmund, O. (2020). A new generation 99 line Matlab code for compliance topology optimization and its extension to 3D. Structural and Multidisciplinary Optimization, 62(4), 2211-2228.
+[^2]:Sigmund, O. (2001). A 99 line topology optimization code written in Matlab. Structural and multidisciplinary optimization, 21(2), 120-127.
+[^3]:Andreassen, E., Clausen, A., Schevenels, M., Lazarov, B. S., & Sigmund, O. (2011). Efficient topology optimization in MATLAB using 88 lines of code. Structural and Multidisciplinary Optimization, 43(1), 1-16.
+[^4]: Svanberg, K. (2002). A class of globally convergent optimization methods based on conservative convex separable approximations. SIAM journal on optimization, 12(2), 555-573.

top_mma/MMA.jl

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+########################################################################################################
+### MMA-Julia (1.7.1)         									
+########################################################################################################
+### MODIFIED BY YULI 2022/5/4                                                            
+########################################################################################################
+export mmasub
+# Loading modules
+using LinearAlgebra
+using SparseArrays
+
+########################################################################################################
+### MMA FUNCTIONS                                                                                    ###
+########################################################################################################
+
+# Function for the MMA sub problem
+function mmasub(m::Int, n::Int, iter::Int, xval::Array{Float64}, xmin::Array{Float64},
+    xmax::Array{Float64}, xold1::Array{Float64}, xold2::Array{Float64}, f0val,
+    df0dx::Array{Float64}, df0dx2:: Array{Float64},
+    fval::Float64,        dfdx::Array{Float64}, dfdx2::Array{Float64},
+    low::Array{Float64}, upp::Array{Float64}, a0::Float64,
+    a::Array{Float64}, c::Array{Float64}, d::Array{Float64})
+
+    # """
+    # This function mmasub performs one MMA-iteration, aimed at solving the nonlinear programming problem: 
+    #
+    # Minimize    f_0(x) + a_0*z + sum( c_i*y_i + 0.5*d_i*(y_i)^2 )
+    # subject to  f_i(x) - a_i*z - y_i < = 0,     i       = 1, ..., m
+    # xmin_j <                           = x_j < = xmax_j, j  = 1,  ..., n
+    # z >                                = 0,     y_i >   = 0, i   = 1,  ..., m
+    # INPUT: 
+    #
+    # m     = The number of general constraints.
+    # n     = The number of variables x_j.
+    # iter  = Current iteration number ( =1 the first time mmasub is called).
+    # xval  = Column vector with the current values of the variables x_j.
+    # xmin  = Column vector with the lower bounds for the variables x_j.
+    # xmax  = Column vector with the upper bounds for the variables x_j.
+    # xold1 = xval, one iteration ago (provided that iter>1).
+    # xold2 = xval, two iterations ago (provided that iter>2).
+    # f0val = The value of the objective function f_0 at xval.
+    # df0dx = Column vector with the derivatives of the objective function
+    #             f_0 with respect to the variables x_j, calculated at xval.
+    # fval = Column vector with the values of the constraint functions f_i, calculated at xval.
+    # dfdx = (m x n)-matrix with the derivatives of the constraint functions
+    #             f_i with respect to the variables x_j, calculated at xval.
+    # dfdx(i,j) = the derivative of f_i with respect to x_j.
+    # low       = Column vector with the lower asymptotes from the previous
+    #             iteration (provided that iter>1).
+    # upp = Column vector with the upper asymptotes from the previous
+    #             iteration (provided that iter>1).
+    # a0 = The constants a_0 in the term a_0*z.
+    # a  = Column vector with the constants a_i in the terms a_i*z.
+    # c  = Column vector with the constants c_i in the terms c_i*y_i.
+    # d  = Column vector with the constants d_i in the terms 0.5*d_i*(y_i)^2.
+    #
+    # OUTPUT: 
+    #
+    # xmma = Column vector with the optimal values of the variables x_j
+    #             in the current MMA subproblem.
+    # ymma = Column vector with the optimal values of the variables y_i
+    #             in the current MMA subproblem.
+    # zmma = Scalar with the optimal value of the variable z
+    #             in the current MMA subproblem.
+    # lam = Lagrange multipliers for the m general MMA constraints.
+    # xsi = Lagrange multipliers for the n constraints alfa_j - x_j < = 0.
+    # eta = Lagrange multipliers for the n constraints x_j - beta_j < = 0.
+    # mu  = Lagrange multipliers for the m constraints -y_i <         = 0.
+    # zet = Lagrange multiplier for the single constraint -z <        = 0.
+    # s   = Slack variables for the m general MMA constraints.
+    # low = Column vector with the lower asymptotes,                   calculated and used
+    #             in the current MMA subproblem.
+    # upp = Column vector with the upper asymptotes, calculated and used
+    #             in the current MMA subproblem.
+    # """
+
+    epsimin = sqrt(m + n) * 10^(-9)
+    feps    = 0.000001
+    asyinit = 0.5
+    asyincr = 1.05
+    asydecr = 0.65
+    albefa  = 0.1
+    een     = ones(n, 1)
+    zeron   = zeros(n, 1)
+
+
+    if iter <= 2
+       low    = xval - asyinit .* (xmax - xmin)
+       upp    = xval + asyinit .* (xmax - xmin)
+    else
+               zzz                    = (xval - xold1) .* (xold1 - xold2)
+               factor                 = copy(een)
+        factor[findall(zzz .> 0.0)]  .= asyincr
+        factor[findall(zzz .< 0.0)]  .= asydecr
+               low                    = xval - factor .* (xold1 - low)
+               upp                    = xval + factor .* (upp - xold1)
+               lowmin                 = xval - 10.0 .* (xmax - xmin)
+               lowmax                 = xval - 0.01 .* (xmax - xmin)
+               uppmin                 = xval + 0.01 .* (xmax - xmin)
+               uppmax                 = xval + 10.0 .* (xmax - xmin)
+               low                    = max.(low, lowmin)
+               low                    = min.(low, lowmax)
+               upp                    = min.(upp, uppmax)
+               upp                    = max.(upp, uppmin)
+    end
+
+      zzz  = low + albefa .* (xval - low)
+      alfa = max.(zzz, xmin)
+
+      zzz  = upp - albefa .* (upp - xval)
+      beta = min.(zzz, xmax)
+
+
+      ux1    = upp - xval
+      ux2    = ux1 .* ux1
+      ux3    = ux2 .* ux1
+      xl1    = xval - low
+      xl2    = xl1 .* xl1
+      xl3    = xl2 .* xl1
+      ul1    = upp - low
+      ulinv1 = een ./ ul1
+
+      uxinv1 = een ./ ux1
+      xlinv1 = een ./ xl1
+      uxinv3 = een ./ ux3
+      xlinv3 = een ./ xl3
+      diap   = (ux3 .* xl1) ./ (2 * ul1)
+      diaq   = (ux1 .* xl3) ./ (2 * ul1)
+
+            p0                     = copy(zeron)
+            q0                     = copy(zeron)
+    p0[findall(df0dx .> 0.0)] = df0dx[findall(df0dx .> 0.0)]
+            p0                     = p0 + 0.001 .* abs.(df0dx) + feps * ulinv1
+            p0                     = p0 .* ux2
+    #       q0                     = zeron
+    q0[findall(df0dx .< 0.0)] = -df0dx[findall(df0dx .< 0.0)]
+            q0                     = q0 + 0.001 * abs.(df0dx) + feps * ulinv1
+            q0                     = q0 .* xl2
+            dg0dx2                 = 2 * (p0 ./ ux3 + q0 ./ xl3)
+            del0                   = df0dx2 - dg0dx2
+            delpos0                = copy(zeron)
+    delpos0[findall(del0 .> 0.0)]  = del0[findall(del0 .> 0.0)]
+            p0                     = p0 + delpos0 .* diap
+            q0                     = q0 + delpos0 .* diaq
+            P                      = spzeros(m, n)
+    P[findall(dfdx .> 0.0)]  = dfdx[findall(dfdx .> 0.0)]
+    #       P                      = P * diag(ux2);
+    #       P                      = P * spdiagm(n, n, 0 => ux2)
+            P                      = P * sparse(Diagonal(ux2[:]))
+            Q                      = spzeros(m, n)
+    Q[findall(dfdx .< 0.0)]  = -dfdx[findall(dfdx .< 0.0)]
+    #       Q                      = Q * diag(xl2);
+    #       Q                      = Q * spdiagm(n, n, 0 => xl2)
+            Q                      = Q * sparse(Diagonal(xl2[:]))
+    #       dgdx2                  = 2.0*(P*diag(uxinv3) + Q*diag(xlinv3));
+    #       dgdx2                  = P * spdiagm(n, n, 0 => uxinv3) + Q * spdiagm(n, n, 0 => xlinv3)
+            dgdx2                  = P * sparse(Diagonal(uxinv3[:])) + Q * sparse(Diagonal(xlinv3[:]))
+            dgdx2                  = 2.0 * dgdx2
+            del                    = dfdx2 - dgdx2
+            delpos                 = zeros(m, n)
+     delpos[findall(del .> 0.0)]   = del[findall(del .> 0.0)]
+    #       P                      = P + delpos*diag(diap);
+    #       P                      = P + delpos * spdiagm(n, n, 0 => diap)
+            P                      = P + delpos * sparse(Diagonal(diap[:]))
+    #       Q                      = Q + delpos*diag(diaq);
+    #       Q                      = Q + delpos * spdiagm(n, n, 0 => diap)
+            Q                      = Q + delpos * sparse(Diagonal(diap[:]))
+            b                      = P * uxinv1 + Q * xlinv1 .- fval
+
+
+    # b  = P * uxinv + Q * xlinv - fval
+    # Solving the subproblem by a primal-dual Newton method
+    xmma, ymma, zmma, lam, xsi, eta, mu, zet, s = 
+        subsolv(m, n, epsimin, low, upp, alfa, beta, p0, q0, P, Q, a0, a, b, c, d)
+    # Return values
+    return xmma, ymma, zmma, lam, xsi, eta, mu, zet, s, low, upp
+end
+
+
+# Function for solving the subproblem (can be used for MMA and GCMMA)
+function subsolv(m::Int, n::Int, epsimin::Float64, low::Array{Float64}, upp::Array{Float64},
+    alfa::Array{Float64}, beta::Array{Float64}, p0::Array{Float64}, q0::Array{Float64},
+    P::Array{Float64}, Q::Array{Float64}, a0::Float64,        a::Array{Float64}, b::Array{Float64},
+    c::Array{Float64}, d::Array{Float64})
+
+    # """
+    # This function subsolv solves the MMA subproblem: 
+    #
+    # minimize SUM[p0j/(uppj-xj) + q0j/(xj-lowj)] + a0*z + SUM[ci*yi + 0.5*di*(yi)^2],
+    #
+    # subject to SUM[pij/(uppj-xj) + qij/(xj-lowj)] - ai*z - yi < = bi,
+    # alfaj <                                                     = xj < = betaj, yi > = 0, z > = 0.
+    #
+    # Input : m,    n,    low,  upp, alfa, beta, p0, q0, P, Q, a0, a, b, c, d.
+    # Output: xmma, ymma, zmma, slack variables and Lagrange multiplers.
+    # """
+
+    een     = ones(Float64, n)
+    eem     = ones(Float64, m)
+    epsi    = 1.0
+    epsvecn = epsi .* een
+    epsvecm = epsi .* eem
+    x       = 0.5 .* (alfa + beta)
+    y       = copy(eem)
+    z       = 1.0
+    lam     = copy(eem)
+    xsi     = een ./ (x - alfa)
+    xsi     = max.(xsi, een)
+    eta     = een ./ (beta - x)
+    eta     = max.(eta, een)
+    mu      = max.(eem, 0.5 .* c)
+    zet     = 1.0
+    s       = copy(eem)
+    itera   = 0
+
+    while epsi > epsimin    # Start while epsi>epsimin
+        epsvecn = epsi .* een
+        epsvecm = epsi .* eem
+        ux1     = upp - x
+        xl1     = x - low
+        ux2     = ux1 .* ux1
+        xl2     = xl1 .* xl1
+        uxinv1  = een ./ ux1
+        xlinv1  = een ./ xl1
+
+        plam = p0 + (P)' * lam
+        qlam = q0 + (Q)' * lam
+        gvec   = P * uxinv1 + Q * xlinv1
+        dpsidx = (plam ./ ux2) - (qlam ./ xl2)
+
+        rex = dpsidx - xsi + eta
+        rey = c + d .* y - mu - lam
+        rez = a0 .- zet .- (a)' * lam
+        relam = gvec - a .* z - y + s - b
+        rexsi = xsi .* (x - alfa) - epsvecn
+        reeta = eta .* (beta - x) - epsvecn
+        remu  = mu .* y - epsvecm
+        rezet = zet * z - epsi
+        res   = lam .* s - epsvecm
+
+        residu1 = [rex' rey' rez]'
+        residu2 = [relam' rexsi' reeta' remu' rezet res']'
+        residu = [residu1' residu2']'
+        residunorm = norm(residu, 2)
+        residumax  = maximum(abs.(residu))
+
+        ittt = 0
+        while (residumax > 0.9 * epsi) && (ittt < 100) # Start while (residumax>0.9*epsi) and (ittt<100)
+            ittt  = ittt + 1
+            itera = itera + 1
+
+            if ittt == 100
+                println("max inner iter reached")
+            end
+            ux1    = upp - x
+            xl1    = x - low
+            ux2    = ux1 .* ux1
+            xl2    = xl1 .* xl1
+            ux3    = ux1 .* ux2
+            xl3    = xl1 .* xl2
+            uxinv1 = een ./ ux1
+            xlinv1 = een ./ xl1
+            uxinv2 = een ./ ux2
+            xlinv2 = een ./ xl2
+            plam   = p0 + (P)' * lam
+            qlam = q0 + (Q)' * lam
+              gvec      = P * uxinv1 + Q * xlinv1
+            # GG        = P .* transpose(uxinv2) - Q .* transpose(xlinv2)
+            # GG        = P .* spdiagm(n,n, 0 => uxinv2) - Q .* spdiagm(n, n, 0 => xlinv2)
+              GG        = P * sparse(Diagonal(uxinv2[:])) - Q * sparse(Diagonal(xlinv2[:]))
+              dpsidx    = (plam ./ ux2) - (qlam ./ xl2)
+              delx      = dpsidx - epsvecn ./ (x - alfa) + epsvecn ./ (beta - x)
+              dely      = c + d .* y - lam - epsvecm ./ y
+              delz      = a0 .- transpose(a) * lam .- epsi / z
+              dellam    = gvec - a .* z - y - b + epsvecm ./ lam
+              diagx     = plam ./ ux3 + qlam ./ xl3
+              diagx     = 2.0 .* diagx + xsi ./ (x - alfa) + eta ./ (beta - x)
+              diagxinv  = een ./ diagx
+              diagy     = d + mu ./ y
+              diagyinv  = eem ./ diagy
+              diaglam   = s ./ lam
+              diaglamyi = diaglam + diagyinv
+
+            if m < n # Start if m < n
+                blam = dellam .+ dely ./ diagy .- GG * (delx ./ diagx)
+                bb   = [blam' delz]'
+                # Alam = spdiagm(0 => diaglamyi) + (GG .* transpose(diagxinv)) * transpose(GG)
+                # Alam = spdiagm(m, m, 0 => diaglamyi) + (GG .* spdiagm(n, n, 0 => diagxinv)) * transpose(GG)
+                Alam = sparse(Diagonal(diaglamyi[:])) + (GG * sparse(Diagonal(diagxinv[:]))) * (GG)'
+                AA    = [Alam a
+                            a' -zet/z]
+                solut = AA \ bb
+                dlam  = solut[1:m]
+                dz    = solut[m+1]
+                dx    = -delx ./ diagx - ((GG)' * dlam) ./ diagx
+            else
+                diaglamyiinv = eem ./ diaglamyi
+                dellamyi     = dellam + dely ./ diagy
+                # Axx          = spdiagm(0 => diagx) + (transpose(GG) .* transpose(diaglamyiinv)) * GG
+                # Axx          = spdiagm(n,n, 0 => diagx) + (transpose(GG) .* spdiagm(m, m, 0 => diaglamyiinv)) * GG
+                Axx          = sparse(Diagonal(diagx[:])) + ((GG)' .* sparse(Diagonal(diaglamyiinv[:]))) * GG
+                azz          = zet / z + (a)' * (a ./ diaglamyi)
+                axz = -(GG)' * (a ./ diaglamyi)
+                bx = delx + (GG)' * (dellamyi ./ diaglamyi)
+                bz = delz - (a)' * (dellamyi ./ diaglamyi)
+                # AAr1 = [Axx axz]
+                # AAr2 = [transpose(axz) azz]
+                # AA   = [AAr1; AAr2]
+                AA   = [Axx axz
+                        axz' azz]
+                bb = [-bx' -bz]'
+                solut = AA \ bb
+                dx    = solut[1:n]
+                dz    = solut[n+1]
+                dlam  = (GG * dx) ./ diaglamyi - dz .* (a ./ diaglamyi) + dellamyi ./ diaglamyi
+            end # End if m<n
+
+            dy   = -dely ./ diagy + dlam ./ diagy
+            dxsi = -xsi .+ epsvecn ./ (x - alfa) - (xsi .* dx) ./ (x - alfa)
+            deta = -eta .+ epsvecn ./ (beta - x) + (eta .* dx) ./ (beta - x)
+            dmu  = -mu .+ epsvecm ./ y - (mu .* dy) ./ y
+            dzet = -zet .+ epsi / z - zet * dz / z
+            ds   = -s .+ epsvecm ./ lam - (s .* dlam) ./ lam
+            xx   = [y' z lam' xsi' eta' mu' zet s']'
+            dxx  = [dy' dz dlam' dxsi' deta' dmu' dzet ds']'
+            #
+            stepxx    = -1.01 .* dxx ./ xx
+            stmxx     = maximum(stepxx)
+            stepalfa  = -1.01 .* dx ./ (x - alfa)
+            stmalfa   = maximum(stepalfa)
+            stepbeta  = 1.01 .* dx ./ (beta - x)
+            stmbeta   = maximum(stepbeta)
+            stmalbe   = max.(stmalfa, stmbeta)
+            stmalbexx = max.(stmalbe, stmxx)
+            stminv    = max.(stmalbexx, 1.0)
+            steg      = 1.0 / stminv
+            #
+            xold   = copy(x)
+            yold   = copy(y)
+            zold   = copy(z)
+            lamold = copy(lam)
+            xsiold = copy(xsi)
+            etaold = copy(eta)
+            muold  = copy(mu)
+            zetold = copy(zet)
+            sold   = copy(s)
+            #
+            itto    = 0
+            resinew = 2.0 * residunorm
+            # Start: while (resinew>residunorm) and (itto<50)
+            while (resinew > residunorm) && (itto < 50)
+                itto = itto + 1
+
+                x      = xold + steg .* dx
+                y      = yold + steg .* dy
+                z      = zold + steg * dz
+                lam    = lamold + steg .* dlam
+                xsi    = xsiold + steg .* dxsi
+                eta    = etaold + steg .* deta
+                mu     = muold + steg .* dmu
+                zet    = zetold + steg * dzet
+                s      = sold + steg .* ds
+                ux1    = upp - x
+                xl1    = x - low
+                ux2    = ux1 .* ux1
+                xl2    = xl1 .* xl1
+                uxinv1 = een ./ ux1
+                xlinv1 = een ./ xl1
+                plam   = p0 + (P)' * lam
+                qlam   = q0 + (Q)' * lam
+                gvec   = P * uxinv1 + Q * xlinv1
+                dpsidx = plam ./ ux2 - qlam ./ xl2
+
+                rex   = dpsidx - xsi + eta
+                rey   = c .+ d .* y - mu - lam
+                rez   = a0 - zet .- (a)' * lam
+                relam = gvec - a .* z - y + s - b
+                rexsi = xsi .* (x - alfa) - epsvecn
+                reeta = eta .* (beta - x) - epsvecn
+                remu  = mu .* y - epsvecm
+                rezet = zet * z - epsi
+                res   = lam .* s - epsvecm
+
+                residu1 = [rex' rey' rez]'
+                residu2 = [relam' rexsi' reeta' remu' rezet res']'
+                residu = [residu1' residu2']'
+                resinew = norm(residu, 2)
+                steg    = steg / 2.0
+            end # End: while (resinew>residunorm) and (itto<50)
+            residunorm = copy(resinew)
+            residumax  = maximum(abs.(residu))
+            steg       = 2.0 * steg
+        end # End: while (residumax>0.9*epsi) and (ittt<200)
+        epsi = 0.1 * epsi
+    end # End: while epsi>epsimin
+    xmma   = copy(x)
+    ymma   = copy(y)
+    zmma   = copy(z)
+    lamma  = lam
+    xsimma = xsi
+    etamma = eta
+    mumma  = mu
+    zetmma = zet
+    smma   = s
+    # Return values
+    return xmma, ymma, zmma, lamma, xsimma, etamma, mumma, zetmma, smma
+end
+

top_mma/TEST.ipynb

@@ -0,0 +1,770 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# module TopOpt99neo\n",
+    "\n",
+    "# include(\"FastConv.jl/src/FastConv.jl\")\n",
+    "# import .FastConv.fastconv\n",
+    "\n",
+    "using LinearAlgebra, SparseArrays\n",
+    "using Plots\n",
+    "# : heatmap, savefig, @animate\n",
+    "using ImageFiltering: imfilter\n",
+    "using Statistics: mean\n",
+    "\n",
+    "using BenchmarkTools\n",
+    "using Zygote\n",
+    "\n",
+    "BLAS.set_num_threads(1)\n",
+    "\n",
+    "# abstract type top99neo end\n",
+    "\n",
+    "include(\"utils.jl\")\n",
+    "include(\"MMA.jl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mutable struct SetUp\n",
+    "    nelx::Int\n",
+    "    nely::Int\n",
+    "\n",
+    "    eta::Float64\n",
+    "    beta::Int\n",
+    "    betaCnt::NTuple{4,Int}\n",
+    "\n",
+    "    move::Float64\n",
+    "    maxit::Int\n",
+    "    maxchang::Float64\n",
+    "\n",
+    "    pasS::Array{Int}\n",
+    "    pasV::Array{Int}\n",
+    "\n",
+    "    function SetUp()\n",
+    "        nelx = 30\n",
+    "        nely = 10\n",
+    "        # volfrac = 0.3\n",
+    "        maxit = 300\n",
+    "        move = 0.1\n",
+    "        beta = 2\n",
+    "        eta = 0.5\n",
+    "        maxchang = 1e-6\n",
+    "        # penalCnt = {maxit+1, 3, 25, 0.25}\n",
+    "        betaCnt = (1, 32, 10, 2) # continuation scheme on beta parCont = { istart,maxPar,steps,deltaPar }\n",
+    "    \n",
+    "        # elNrs = reshape(1:nely*nelx, nely, nelx)\n",
+    "        # a1 = elNrs[Int.(nely/4:nely/2), Int.(nelx/4:nelx/2)]\n",
+    "        # pasS, pasV = Array([]), a1[:]\n",
+    "        pasS, pasV = Array([]), Array([])\n",
+    "    \n",
+    "        new(nelx, nely, eta, beta, betaCnt, move, maxit, maxchang, pasS, pasV)\n",
+    "    end\n",
+    "\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "setup = SetUp()\n",
+    "# setup.nelx = 20\n",
+    "# setup.nely = 10\n",
+    "# setup.maxit = 100\n",
+    "# typeof(setup.nodeNrs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "mutable struct Mat\n",
+    "    E0::Float64\n",
+    "    Emin::Float64\n",
+    "    ν::Float64\n",
+    "    penal::Float64\n",
+    "    volfrac::Float64\n",
+    "\n",
+    "    function Mat()\n",
+    "        E0 = 1\n",
+    "        Emin = 1e-9\n",
+    "        ν = 0.3\n",
+    "        penal = 3.0\n",
+    "        volfrac = 0.3\n",
+    "        new(E0, Emin, ν, penal, volfrac)\n",
+    "    end\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat = Mat()\n",
+    "# mat.volfrac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mutable struct DiscretizationFeature\n",
+    "    nEl::Int\n",
+    "    nodeNrs::Array{Int,2}\n",
+    "    nDof::Int\n",
+    "    act\n",
+    "\n",
+    "    cMat::Array{Int}\n",
+    "    Iar::Array{Int}\n",
+    "    Ke::Array{Float64,1}\n",
+    "    Ke0::Array{Float64,2}\n",
+    "    \n",
+    "    function DiscretizationFeature(setup::SetUp, mat::Mat)\n",
+    "        nelx = setup.nelx\n",
+    "        nely = setup.nely\n",
+    "        pasS, pasV = setup.pasS, setup.pasV\n",
+    "        ν = mat.ν\n",
+    "    \n",
+    "        nEl = nely * nelx\n",
+    "        nodeNrs = reshape(1:(1+nelx)*(1+nely), nely + 1, nelx + 1)\n",
+    "        nDof = (nely + 1) * (nelx + 1) * 2\n",
+    "        act = setdiff(collect(1:nEl), union(pasS, pasV))\n",
+    "    \n",
+    "        cVec = reshape(2 * nodeNrs[1:end-1, 1:end-1] .+ 1, nEl, 1)\n",
+    "        # cMat = cVec + Int.([0 1 2 * nely .+ [2 3 0 1] -2 -1])\n",
+    "        cMat = Int.(repeat(cVec, 1, 8) + repeat([0 1 2 * nely .+ [2 3 0 1] -2 -1], nelx * nely, 1))\n",
+    "        # nDof = (nely + 1) * (nelx + 1) * 2\n",
+    "        FuckRow = [1 2 3 4 5 6 7 8]\n",
+    "        sI::Array{Int}, sII::Array{Int} = copy(FuckRow), fill(1, 1, 8)\n",
+    "        for j in 2:8\n",
+    "            sI = cat(sI, FuckRow[j:8]'; dims=2)\n",
+    "            # sI = append!(sI, convert(Array{Int},[j:8]))\n",
+    "            # sII = cat(2, sII, repmat(j, 1, 8 - j + 1))\n",
+    "            sII = cat(sII, fill(j, 1, 8 - j + 1); dims=2)\n",
+    "        end\n",
+    "        iK::Array{Int,2}, jK::Array{Int,2} = cMat[:, sI][:, 1, :]', cMat[:, sII][:, 1, :]'\n",
+    "        Iar = sort([iK[:] jK[:]]; dims=2, rev=true) # comma is a newline\n",
+    "        # iK[:], jK[:] .= 0.0, 0.0\n",
+    "        c1 = [12, 3, -6, -3, -6, -3, 0, 3, 12, 3, 0, -3, -6, -3, -6, 12, -3, 0, -3, -6, 3, 12, 3, -6, 3, -6, 12, 3, -6, -3, 12, 3, 0, 12, -3, 12]\n",
+    "        c2 = [-4, 3, -2, 9, 2, -3, 4, -9, -4, -9, 4, -3, 2, 9, -2, -4, -3, 4, 9, 2, 3, -4, -9, -2, 3, 2, -4, 3, -2, 9, -4, -9, 4, -4, -3, -4]\n",
+    "        Ke = 1 / (1 - ν^2) / 24 .* (c1 .+ ν .* c2) # half-KE vector\n",
+    "        # full KE\n",
+    "        # Ke0::Array{Float64} = zeros(8, 8)\n",
+    "        # start_id, end_id = 1, 8\n",
+    "        # for i in 1:8\n",
+    "        #     Ke0[i:8, i] = Ke[start_id:end_id]\n",
+    "        #     start_id, end_id = end_id + 1, 2 * end_id - start_id\n",
+    "        # end\n",
+    "    \n",
+    "        Ke0::Array{Float64} = zeros(8, 8)\n",
+    "        # Index::Array{Int} = [sI' sII']\n",
+    "        # Ke0[sI, sII] = Ke\n",
+    "        Index = findall(isequal(1), tril(ones(8, 8)))\n",
+    "        Ke0[Index] = Ke'\n",
+    "        # Ke0 = reshape(Ke0, 8, 8)\n",
+    "        Ke0 = Ke0 + Ke0' - diagm(diag(Ke0))\n",
+    "        new(nEl, nodeNrs, nDof, act, cMat, Iar, Ke, Ke0)\n",
+    "    end\n",
+    "end\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "disfeature = DiscretizationFeature(setup, mat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mutable struct LoadsSupportsBCs\n",
+    "    # setup::SetUp\n",
+    "    lcDof::Array{Int}\n",
+    "    F\n",
+    "    free::Array{Int}\n",
+    "    fixed::Array{Int}\n",
+    "\n",
+    "    function LoadsSupportsBCs(setup::SetUp, disfeature::DiscretizationFeature)\n",
+    "        nelx = setup.nelx\n",
+    "        nely = setup.nely\n",
+    "        nodeNrs = disfeature.nodeNrs\n",
+    "        nDof = disfeature.nDof\n",
+    "    \n",
+    "        load_position::Symbol = :half_MBB\n",
+    "        if load_position == :half_MBB\n",
+    "            load_nodey, load_nodex = 1, 1\n",
+    "            # fixed = union([1:2:2*(nely+1)], 2 * nodeNrs[nely+1, nelx+1])\n",
+    "            fixed = union(collect(1:2:2*(nely+1)), 2 * nodeNrs[end, end])\n",
+    "        elseif load_position == :cantilever\n",
+    "            load_nodey = nely + 1\n",
+    "            load_nodex = nelx / 2 + 1\n",
+    "            fixed = 1:2*(nely+1)\n",
+    "        end\n",
+    "    \n",
+    "        F = spzeros(nDof)\n",
+    "    \n",
+    "        load_type::Symbol = :pin\n",
+    "        if load_type == :pin # 1 point\n",
+    "            lcDof = collect(2 * nodeNrs[load_nodey, load_nodex])\n",
+    "            F[2, 1] = -1.0\n",
+    "        elseif load_type == :points # 5 points\n",
+    "            lcDof = collect(2 * nodeNrs[load_nodey, load_nodex], nodeNrs[load_nodey, load_nodex-1], nodeNrs[load_nodey, load_nodex-2], nodeNrs[load_nodey, load_nodex+1], nodeNrs[load_nodey, load_nodex+2])\n",
+    "            F[lcDof', ones(length(lcDof'))] .= -1.0\n",
+    "        elseif load_type == :line\n",
+    "            lcDof = [2:2*(nely+1):nDof]\n",
+    "            F = spzeros(nDof, 1)\n",
+    "            # F = sparse(lcDof', ones(length(lcDof')), -1.0)\n",
+    "        end\n",
+    "        all = collect(1:nDof)\n",
+    "        free = setdiff(all, fixed)\n",
+    "    \n",
+    "        new(lcDof, F, free, fixed)\n",
+    "    end\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load = LoadsSupportsBCs(setup, disfeature)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mutable struct Initialization \n",
+    "    # setup::SetUp\n",
+    "    x::Array{Float64}\n",
+    "    xPhys::Array{Float64}\n",
+    "    xOld::Array{Float64}\n",
+    "    ch::Float64\n",
+    "    loop::Int\n",
+    "    # U::Array{Float64}\n",
+    "    # dsK::Array{Float64}\n",
+    "    # dV::Array{Float64}\n",
+    "\n",
+    "    function Initialization(setup::SetUp, disfeature::DiscretizationFeature, mat::Mat)\n",
+    "        pasV = setup.pasV\n",
+    "        pasS = setup.pasS\n",
+    "        act = disfeature.act\n",
+    "        volfrac = mat.volfrac\n",
+    "        nEl = disfeature.nEl\n",
+    "        nDof = disfeature.nDof\n",
+    "        # column vectors\n",
+    "        x = zeros(nEl, 1)\n",
+    "        # dV[act, 1] .= 1.0 / nEl / volfrac\n",
+    "        x[act] .= volfrac\n",
+    "        x[pasS] .= 1.0\n",
+    "        xPhys, xOld, ch, loop = copy(x), ones(nEl, 1), 1.0, 0\n",
+    "        # x̅  x̃\n",
+    "    \n",
+    "        new(x, xPhys, xOld, ch, loop)\n",
+    "    end\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ini = Initialization(setup, disfeature, mat)\n",
+    "# typeof(ini.x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mutable struct Filter \n",
+    "    rmin::Float64\n",
+    "    ft::Int\n",
+    "    h::Array{Float64}\n",
+    "    Hs::Array{Float64}\n",
+    "    dHs::Array{Float64}\n",
+    "\n",
+    "    function Filter(setup::SetUp)\n",
+    "        nelx = setup.nelx\n",
+    "        nely = setup.nely\n",
+    "        # bcF = setup.bcF\n",
+    "        rmin = 6.5\n",
+    "        ft = 3\n",
+    "        dy, dx = meshgrid(-ceil(rmin)+1:ceil(rmin)-1, -ceil(rmin)+1:ceil(rmin)-1)\n",
+    "        h = max.(0, rmin .- sqrt.(dx .^ 2 + dy .^ 2))\n",
+    "        Hs = imfilter(ones(nely, nelx), h, \"symmetric\")\n",
+    "        dHs = Hs\n",
+    "        new(rmin, ft, h, Hs, dHs)\n",
+    "    end\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "filter = Filter(setup)\n",
+    "# filter.Hs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FiniteElementAnalasys (generic function with 1 method)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "function FiniteElementAnalasys(mat::Mat, disfeature::DiscretizationFeature, load::LoadsSupportsBCs, xPhys::Array{Float64})\n",
+    "        nEl, nDof, Iar, Ke = disfeature.nEl, disfeature.nDof, disfeature.Iar, disfeature.Ke\n",
+    "        act = disfeature.act\n",
+    "        Emin, penal, E0 = mat.Emin, mat.penal, mat.E0\n",
+    "        F, free = load.F, load.free\n",
+    "\n",
+    "        sK = Emin .+ xPhys .^ penal .* (E0 - Emin)\n",
+    "        sK = reshape(Ke[:] * sK', length(Ke) * nEl, 1)\n",
+    "        K = sparse(Iar[:, 1], Iar[:, 2], vec(sK), nDof, nDof)\n",
+    "        U = zeros(nDof)\n",
+    "        #~\n",
+    "        # U[free] = cholesky(Symmetric(K[free, free], :L), check=false) \\ F[free]\n",
+    "        K0 = K + K' - diagm(diag(K))\n",
+    "        U[free] = K0[free, free] \\ F[free]\n",
+    "        #~\n",
+    "        Obj = F' * U\n",
+    "        Vf = mean(xPhys[act])\n",
+    "        return U, Obj, Vf\n",
+    "end\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1-element Vector{Float64}:\n",
+       " 4558.123953305818"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "U, C, vf = FiniteElementAnalasys(mat, disfeature, load, xval)\n",
+    "C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "function SensitivityAnalasys(setup::SetUp, filter::Filter, mat::Mat, disfeature::DiscretizationFeature, \n",
+    "    U::Array{Float64}, xPhys::Array{Float64})\n",
+    "    nelx, nely, act = setup.nelx, setup.nely, disfeature.act\n",
+    "    dHs, h = filter.dHs, filter.h\n",
+    "    E0, Emin, penal = mat.E0, mat.Emin, mat.penal\n",
+    "    nEl, cMat, Ke0 = disfeature.nEl, disfeature.cMat, disfeature.Ke0\n",
+    "    act = disfeature.act\n",
+    "\n",
+    "    dsK, dV = zeros(nEl, 1), zeros(nEl, 1)\n",
+    "    dV[act, 1] .= 1.0 / length(act)\n",
+    "    dsK[act] = -penal * (E0 - Emin) .* xPhys[act] .^ (penal - 1)\n",
+    "\n",
+    "    dc = dsK .* sum((U[cMat] * Ke0) .* U[cMat], dims=2)\n",
+    "    dc = imfilter(reshape(dc, nely, nelx) ./ dHs, h, \"symmetric\")\n",
+    "    dV0 = imfilter(reshape(dV, nely, nelx) ./ dHs, h, \"symmetric\")\n",
+    "\n",
+    "    return reshape(dc, nEl, 1), reshape(dV0, nEl, 1)\n",
+    "end\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "function AutomaticDifferentiation(mat::Mat, disfeature::DiscretizationFeature, load::LoadsSupportsBCs, xPhys::Array{Float64})\n",
+    "    # fea = FiniteElementAnalasys()\n",
+    "    dc_AD = gradient(x -> FiniteElementAnalasys(mat, disfeature, load, x), xPhys)\n",
+    "    # dv_AD = gradient(x)\n",
+    "\n",
+    "    return dc_AD\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xval = ini.x\n",
+    "dc_AD = AutomaticDifferentiation(mat, disfeature, load, xval)\n",
+    "typeof(dc_AD)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "typeof(dc_AD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "function Visualization(setup::SetUp, x::Array{Float64}, loop::Int)\n",
+    "    nelx, nely = setup.nelx, setup.nely\n",
+    "    # cmap = cgrad(:Blues_9, rev=false)\n",
+    "    plot = heatmap(reshape(x, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400,nely/nelx*400) , legend=:none, display_type=:gui)\n",
+    "    display(plot)\n",
+    "    savefig(plot, \"./top/res_$loop.pdf\")\n",
+    "    # PLOT FINAL DESIGN\n",
+    "    # heatmap(1.0 .- x[end:-1:1, :], yaxis=false, xaxis=false, legend=:none,color=:greys, grid=false, border=nothing, aspect_ratio=:equal)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "function Optimization(setup::SetUp, mat::Mat, load::LoadsSupportsBCs,\n",
+    "     filter::Filter, ini::Initialization, disfeature::DiscretizationFeature)\n",
+    "    ch, loop = ini.ch, ini.loop\n",
+    "    x, xPhys, xOld = ini.x, ini.xPhys, ini.xOld\n",
+    "\n",
+    "    maxit, maxchang = setup.maxit, setup.maxchang\n",
+    "    nely, nelx = setup.nely, setup.nelx\n",
+    "    eta, beta, betaCnt = setup.eta, setup.beta, setup.betaCnt\n",
+    "    volfrac, penal, nEl = mat.volfrac, mat.penal, disfeature.nEl\n",
+    "    act = disfeature.act\n",
+    "\n",
+    "    Hs, h, ft = filter.Hs, filter.h, filter.ft\n",
+    "\n",
+    "    opt_hist = []\n",
+    "    vf_hist = []\n",
+    "\n",
+    "    ### Initiation of MMA ###\n",
+    "    xval  = copy(x[act])\n",
+    "    xold1 = copy(xval)\n",
+    "    xold2 = copy(xold1)\n",
+    "\n",
+    "    low = copy(xval)\n",
+    "    upp = copy(low)\n",
+    "    #########################\n",
+    "\n",
+    "    anim = @animate while ch > maxchang && loop < maxit || beta < betaCnt[2]\n",
+    "        @time begin\n",
+    "            loop = loop + 1\n",
+    "            # COMPUTE PHYSICAL DENSITY FIELD \n",
+    "            xTilde = imfilter(reshape(x, nely, nelx), h, \"symmetric\") ./ Hs\n",
+    "            xPhys[act] = copy(xTilde[act])\n",
+    "            if ft > 1\n",
+    "                f = (mean(prj(xPhys[act], eta, beta)) .- volfrac) * (ft == 3)\n",
+    "                while abs(f) > maxchang\n",
+    "                    eta = eta - f / mean(deta(xPhys[:], eta, beta))\n",
+    "                    f = mean(prj(xPhys[act], eta, beta)) - volfrac\n",
+    "                end\n",
+    "                filter.dHs = Hs ./ reshape(dprj(xTilde, eta, beta), nely, nelx)\n",
+    "                xPhys = prj(xPhys, eta, beta)\n",
+    "            end\n",
+    "            ch = norm(xPhys - xOld) ./ sqrt(nEl)\n",
+    "            xOld = copy(xPhys)\n",
+    "            #~ SETUP AND SOLVE EQUILIBRIUM EQUATIONS\n",
+    "            U, C, Vf = FiniteElementAnalasys(mat, disfeature, load, xPhys)\n",
+    "            push!(opt_hist, C)\n",
+    "            push!(vf_hist, Vf)\n",
+    "            #~ COMPUTE SENSITIVITIES\n",
+    "            dc, dV0 = SensitivityAnalasys(setup, filter, mat, disfeature, U, xPhys)\n",
+    "            #~ MMA iteration\n",
+    "            xmma, low, upp = MMAupdate(xval, low, upp, xold1, xold2, C, Vf, dc, dV0, loop, Mat().volfrac)\n",
+    "            # Some vectors are updated:\n",
+    "            xold2 = copy(xold1)\n",
+    "            xold1 = copy(xval)\n",
+    "            xval = copy(xmma)\n",
+    "\n",
+    "            x[act] = xval\n",
+    "\n",
+    "            #~ CONTINUATION\n",
+    "            beta = cnt(beta, betaCnt, loop, ch, maxchang)\n",
+    "\n",
+    "            heatmap(reshape(xPhys, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none)\n",
+    "\n",
+    "        end\n",
+    "        if mod(loop, 10) == 0\n",
+    "            println(\"It.: $loop C.: $C Vf.: $Vf ch.: $ch, p.: $penal beta.:$beta eta.: $eta \")\n",
+    "            Visualization(setup, xPhys, loop)\n",
+    "        end\n",
+    "    end\n",
+    "    gif(anim, \"./top/top_hist.gif\", fps=8)\n",
+    "    return xPhys, opt_hist, vf_hist, anim\n",
+    "end\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @time xPhys, opt_hist, vf_hist, loop = Optimization(setup, mat, load, filter, ini, disfeature)\n",
+    "Optimization(setup, mat, load, filter, ini, disfeature)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "function MMAupdate(xval::Array{Float64}, low::Array{Float64}, upp::Array{Float64}, \n",
+    "    xold1::Array{Float64}, xold2::Array{Float64},\n",
+    "    Obj::Float64, Vf::Float64, dc::Array{Float64}, dV0::Array{Float64}, loop::Int, volfrac::Float64)\n",
+    "    move = 0.1\n",
+    "    ### Initiation of MMA ###\n",
+    "    m = 1\n",
+    "    # active design variable\n",
+    "    n = length(act)\n",
+    "    onen = ones(n, 1)\n",
+    "    onem = ones(m, 1)\n",
+    "    zeron = zeros(n, 1)\n",
+    "    zerom = zeros(m, 1)\n",
+    "    a_mma = zerom\n",
+    "    c_mma = 1.0e3 * onem\n",
+    "    d_mma = zerom\n",
+    "    a0 = 1.0\n",
+    "    # column vector\n",
+    "    # xval = xval\n",
+    "    xmin = max.(xval .- move, zeron)\n",
+    "    xmax = min.(xval .+ move, onen)\n",
+    "\n",
+    "    # low = low\n",
+    "    # upp = upp\n",
+    "    # objective function   \n",
+    "    f0val = Obj\n",
+    "    df0dx = dc[act]\n",
+    "    df0dx2 = 0.0 * df0dx\n",
+    "    # constraint function\n",
+    "    fval = Vf / volfrac  - 1.0  # column vector\n",
+    "    dfdx = reshape(dV0[act], 1, length(act)) ./ volfrac  # (m * n)\n",
+    "    dfdx2 = 0.0 * dfdx\n",
+    "\n",
+    "    # The MMA subproblem is solved at the point xval:\n",
+    "    xmma, ymma, zmma, lam, xsi, eta, mu, zet, s, low, upp =\n",
+    "        mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,\n",
+    "            f0val, df0dx, df0dx2, fval, dfdx, dfdx2, low, upp, a0, a_mma, c_mma, d_mma)\n",
+    "           \n",
+    "    return xmma, low, upp\n",
+    "    # xmma =\n",
+    "    # mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,\n",
+    "    #     f0val, df0dx, df0dx2, fval, dfdx, dfdx2, low, upp, a0, a_mma, c_mma, d_mma)\n",
+    "    #     return xmma\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "include(\"MMA.jl\")\n",
+    "### Initiation of MMA ###\n",
+    "x = ones(disfeature.nEl, 1) * 0.5\n",
+    "act = disfeature.act\n",
+    "xval  = copy(x[act])\n",
+    "xold1 = copy(xval)\n",
+    "xold2 = copy(xold1)\n",
+    "\n",
+    "low = copy(xval)\n",
+    "upp = copy(low)\n",
+    "\n",
+    "xval, low, upp = MMAupdate(xval, low, upp, xold1, xold2, 1.0, 0.3, x[act],\n",
+    " reshape(x[act], 1, length(act)) , 1, Mat().volfrac)\n",
+    "# ux2 = MMAupdate(xval, low, upp, xold1, xold2, 1.0, 0.3, x[act],\n",
+    "#  reshape(x[act], 1, length(act)) , 1, Mat().volfrac)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size(xval)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using NLopt\n",
+    "\n",
+    "function myfunc(x::Vector, grad::Vector)\n",
+    "    if length(grad) > 0\n",
+    "        grad[1] = 0\n",
+    "        grad[2] = 0.5/sqrt(x[2])\n",
+    "    end\n",
+    "    return sqrt(x[2])\n",
+    "end\n",
+    "\n",
+    "function myconstraint(x::Vector, grad::Vector, a, b)\n",
+    "    if length(grad) > 0\n",
+    "        grad[1] = 3a * (a*x[1] + b)^2\n",
+    "        grad[2] = -1\n",
+    "    end\n",
+    "    (a*x[1] + b)^3 - x[2]\n",
+    "end\n",
+    "\n",
+    "opt = Opt(:LD_MMA, 2)\n",
+    "opt.lower_bounds = [-Inf, 0.]\n",
+    "opt.xtol_rel = 1e-4\n",
+    "\n",
+    "opt.min_objective = myfunc\n",
+    "inequality_constraint!(opt, (x,g) -> myconstraint(x,g,2,0), 1e-8)\n",
+    "inequality_constraint!(opt, (x,g) -> myconstraint(x,g,-1,1), 1e-8)\n",
+    "\n",
+    "(minf,minx,ret) = optimize(opt, [1.234, 5.678])\n",
+    "numevals = opt.numevals # the number of function evaluations\n",
+    "println(\"got $minf at $minx after $numevals iterations (returned $ret)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using NLopt\n",
+    "function update_NLopt(act, xval, low, upp, C, Vf, dc, dV0)\n",
+    "    alg = \":LD_MMA\"\n",
+    "    n = length(act)\n",
+    "    opt = Opt(alg, n)\n",
+    "    opt.lower_bounds = low\n",
+    "    opt.upper_bounds = upp\n",
+    "\n",
+    "    opt.min_objective = ObjectiveFunction\n",
+    "\n",
+    "    inequality_constraint!(opt, (x, g) -> VolumeConstraint(x, g;), 1e-8)\n",
+    "\n",
+    "    return\n",
+    "end\n",
+    "\n",
+    "function ObjectiveFunction(x, grad; act, obj, dobj)\n",
+    "    if length(grad) > 0\n",
+    "        grad[:] = dobj[act]\n",
+    "    end\n",
+    "    return obj\n",
+    "end\n",
+    "\n",
+    "function VolumeConstraint(x, g; )\n",
+    "    if length(grad) > 0\n",
+    "        grad[:] = dV0\n",
+    "    end\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f(x::Matrix, y) = sum(sin, x) + prod(tan, x) * sum(sqrt, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Zygote\n",
+    "grad = gradient(y -> f(y), rand(3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "typeof(grad)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.7.2",
+   "language": "julia",
+   "name": "julia-1.7"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

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top_mma/top99neo_mma.jl

@@ -0,0 +1,402 @@
+# __precompile__()
+# module TopOpt99neo_MMA
+
+using LinearAlgebra, SparseArrays
+using Plots
+      #             : heatmap, savefig, @animate
+using ImageFiltering: imfilter
+using Statistics: mean
+
+using BenchmarkTools
+
+BLAS.set_num_threads(1)
+
+# abstract type top99neo end
+
+include("utils.jl")
+include("MMA.jl")
+
+export SetUp, Mat, DiscretizationFeature, LoadsSupportsBCs, Initialization, Filter
+export Optimization, Visualization
+
+mutable struct SetUp
+    nelx:: Int
+    nely:: Int
+
+    eta:: Float64
+    beta:: Int
+    penalCnt:: Array
+    betaCnt:: Array
+
+    move:: Float64
+    maxit:: Int
+    maxchang:: Float64
+
+    pasS:: Array{Int}
+    pasV:: Array{Int}
+
+    function SetUp()
+          nelx                                = 120
+          nely                                = 40
+        # volfrac                             = 0.3
+          maxit                               = 500
+          move                                = 0.1
+          beta                                = 2
+          eta                                 = 0.5
+          maxchang                            = 1e-6
+          penalCnt                            = [1, 3, 20, 0.2]
+          betaCnt                             = [1, 32, 10, 2]
+        # continuation scheme on beta parCont = { istart,maxPar,steps,deltaPar }
+
+        #    elNrs = reshape(1:nely*nelx, nely, nelx)
+        #    a1    = elNrs[Int.(nely/4:nely/2), Int.(nelx/4:nelx/2)]
+        #    pasS,  pasV       = Array([]), a1[:]
+        pasS, pasV = Array([]), Array([])
+
+        new(nelx, nely, eta, beta, penalCnt, betaCnt, move, maxit, maxchang, pasS, pasV)
+    end
+
+end
+
+mutable struct Mat
+    E0:: Float64
+    Emin:: Float64
+    ν:: Float64
+    penal:: Float64
+    volfrac:: Float64
+
+    function Mat()
+        E0      = 1.0
+        Emin    = 1e-9 * E0
+        ν       = 0.3
+        penal   = 3.0
+        volfrac = 0.3
+        new(E0, Emin, ν, penal, volfrac)
+    end
+end
+
+struct DiscretizationFeature
+    nEl:: Int
+    nodeNrs:: Array{Int,2}
+    nDof:: Int
+    act
+
+    cMat:: Array{Int}
+    Iar:: Array{Int}
+    Ke:: Array{Float64,1}
+    Ke0:: Array{Float64,2}
+
+    function DiscretizationFeature(setup::SetUp, mat::Mat)
+             nelx  = setup.nelx
+             nely  = setup.nely
+        pasS, pasV = setup.pasS, setup.pasV
+             ν     = mat.ν
+
+        nEl     = nely * nelx
+        nodeNrs = reshape(1:(1+nelx)*(1+nely), nely + 1, nelx + 1)
+        nDof    = (nely + 1) * (nelx + 1) * 2
+        act     = setdiff(collect(1:nEl), union(pasS, pasV))
+
+        cVec = reshape(2 * nodeNrs[1:end-1, 1:end-1] .+ 1, nEl, 1)
+        cMat = Int.(repeat(cVec, 1, 8) + repeat([0 1 2 * nely .+ [2 3 0 1] -2 -1], nelx * nely, 1))
+
+            FuckRow    = [1 2 3 4 5 6 7 8]
+        sI::Array{Int}, sII::Array{Int} = copy(FuckRow), fill(1, 1, 8)
+        @inbounds for j in 2: 8
+              sI  = cat(sI, FuckRow[j:8]'; dims=2)
+            # sII = cat(2, sII, repmat(j, 1, 8 - j + 1))
+              sII = cat(sII, fill(j, 1, 8 - j + 1); dims=2)
+        end
+        iK::Array{Int,2}, jK::Array{Int,2} = cMat[:, sI][:, 1, :]', cMat[:, sII][:, 1, :]'
+            Iar          = sort([iK[:] jK[:]]; dims=2, rev=true) # comma is a newline
+        # iK[:], jK[:] .= 0.0, 0.0
+            c1           = [12, 3, -6, -3, -6, -3, 0, 3, 12, 3, 0, -3, -6, -3, -6, 12, -3, 0, -3, -6, 3, 12, 3, -6, 3, -6, 12, 3, -6, -3, 12, 3, 0, 12, -3, 12]
+            c2           = [-4, 3, -2, 9, 2, -3, 4, -9, -4, -9, 4, -3, 2, 9, -2, -4, -3, 4, 9, 2, 3, -4, -9, -2, 3, 2, -4, 3, -2, 9, -4, -9, 4, -4, -3, -4]
+            Ke           = 1 / (1 - ν^2) / 24 .* (c1 .+ ν .* c2) # half-KE vector
+        # full KE
+        # Ke0::Array{Float64} = zeros(8, 8)
+        # start_id,            end_id = 1, 8
+        # for i in 1: 8
+        # Ke0[i:8, i] = Ke[start_id:end_id]
+        # start_id,    end_id = end_id + 1, 2 * end_id - start_id
+        # end
+
+        Ke0::Array{Float64}    = zeros(8, 8)
+        #    Index::Array{Int} = [sI' sII']
+        #    Ke0[sI, sII]      = Ke
+             Index             = findall(isequal(1), tril(ones(8, 8)))
+        Ke0[Index]            = Ke'
+        # Ke0 = reshape(Ke0, 8, 8)
+          Ke0 = Ke0 + Ke0' - diagm(diag(Ke0))  
+        # diag return the diag of a matrix
+        # diagm build a matirx with a diag
+        new(nEl, nodeNrs, nDof, act, cMat, Iar, Ke, Ke0)
+    end
+end
+
+mutable struct LoadsSupportsBCs
+    lcDof:: Array{Int}
+    F
+    free:: Array{Int}
+    fixed:: Array{Int}
+
+    function LoadsSupportsBCs(setup::SetUp, disfeature::DiscretizationFeature)
+        nelx    = setup.nelx
+        nely    = setup.nely
+        nodeNrs = disfeature.nodeNrs
+        nDof    = disfeature.nDof
+
+        load_position::Symbol          = :half_MBB
+        if             load_position  == :half_MBB
+        load_nodey    , load_nodex     = 1, 1
+        #              fixed           = union([1:2:2*(nely+1)], 2 * nodeNrs[nely+1, nelx+1])
+                       fixed           = union(collect(1:2:2*(nely+1)), 2 * nodeNrs[end, end])
+        elseif         load_position  == :cantilever
+                       load_nodey      = nely + 1
+                       load_nodex      = nelx / 2 + 1
+                       fixed           = 1:2*(nely+1)
+        end
+
+        F = spzeros(nDof)
+
+        load_type::Symbol                          = :pin
+        if         load_type == :pin # 1 point
+                   lcDof                           = collect(2 * nodeNrs[load_nodey, load_nodex])
+        F[2]                           = -1.0
+        elseif     load_type == :points # 5 points
+        #          lcDof                           = collect(2 * nodeNrs[load_nodey, load_nodex], nodeNrs[load_nodey, load_nodex-1], nodeNrs[load_nodey, load_nodex-2], nodeNrs[load_nodey, load_nodex+1], nodeNrs[load_nodey, load_nodex+2])
+        F[lcDof'] .= -1.0
+        elseif     load_type == :line
+                   lcDof                           = [2:2*(nely+1):nDof]
+                   F                               = spzeros(nDof, 1)
+        F[lcDof'] .= -1.0
+        #          F                               = sparse(lcDof', ones(length(lcDof')), -1.0)
+        end
+        all  = collect(1:nDof)
+        free = setdiff(all, fixed)
+
+        new(lcDof, F, free, fixed)
+    end
+end
+
+struct Initialization
+    x:: Array{Float64}
+    xPhys:: Array{Float64}
+    xOld:: Array{Float64}
+    ch:: Float64
+    loop:: Int
+
+    function Initialization(setup::SetUp, disfeature::DiscretizationFeature, mat::Mat)
+          pasV    = setup.pasV
+          pasS    = setup.pasS
+          act     = disfeature.act
+          volfrac = mat.volfrac
+          nEl     = disfeature.nEl
+        # nDof    = disfeature.nDof
+        # column vectors
+              x       = zeros(nEl, 1)
+        x[act] .= volfrac
+        x[pasS] .= 1.0
+        xPhys, xOld,   ch, loop = copy(x), ones(nEl, 1), 1.0, 0
+        # x̅  x̃
+
+        new(x, xPhys, xOld, ch, loop)
+    end
+end
+
+mutable struct Filter
+    rmin:: Float64
+    ft:: Int
+    h:: Array{Float64}
+    Hs:: Array{Float64}
+    dHs:: Array{Float64}
+
+    function Filter(setup::SetUp)
+           nelx    = setup.nelx
+           nely    = setup.nely
+        #  volfrac = mat.volfrac
+        #  bcF     = setup.bcF
+           rmin    = 6.5
+           ft      = 3 # 1 density 2 projection 3 volume preserving
+        dy, dx     = meshgrid(-ceil(rmin)+1:ceil(rmin)-1, -ceil(rmin)+1:ceil(rmin)-1)
+           h       = max.(0, rmin .- sqrt.(dx .^ 2 + dy .^ 2))
+           Hs      = imfilter(ones(nely, nelx), h, "symmetric")
+           dHs     = Hs
+
+        new(rmin, ft, h, Hs, dHs)
+    end
+end
+
+
+function FiniteElementAnalasys(mat::Mat, disfeature::DiscretizationFeature, load::LoadsSupportsBCs, xPhys::Array{Float64})
+    nEl , nDof,  Iar,     Ke        = disfeature.nEl, disfeature.nDof, disfeature.Iar, disfeature.Ke
+         act    = disfeature.act
+    Emin, penal, E0      = mat.Emin, mat.penal,       mat.E0
+    F   , free  = load.F, load.free
+    # Interplotation model
+      sK      = Emin .+ xPhys .^ penal .* (E0 - Emin)
+      sK      = reshape(Ke[:] * sK', length(Ke) * nEl, 1)
+      K       = sparse(Iar[:, 1], Iar[:, 2], vec(sK), nDof, nDof)
+      U       = zeros(nDof)
+    # U[free] = cholesky(Symmetric(K[free, free], :L), check=false) \ F[free
+    U[free] = cholesky(Symmetric(K, :L)[free, free], check=false) \ F[free]
+    Obj   = F' * U
+    Vf = sum(xPhys[act])/length(act)
+    return U, Obj, Vf
+end
+
+function SensitivityAnalasys(setup::SetUp, filter::Filter, mat::Mat, disfeature::DiscretizationFeature, U::Array{Float64}, xPhys::Array{Float64})
+    nelx, nely, act         = setup.nelx,     setup.nely,      disfeature.act
+    dHs , h    = filter.dHs, filter.h
+    E0  , Emin, penal       = mat.E0,         mat.Emin,  mat.penal
+    nEl , cMat, Ke0         = disfeature.nEl, disfeature.cMat, disfeature.Ke0
+         act   = disfeature.act
+
+    dsK, dV    = zeros(nEl), zeros(nEl)
+    dV[act] .= 1.0/length(act)
+    dsK[act]   = -penal * (E0 - Emin) .* xPhys[act] .^ (penal - 1)
+
+    dc  = dsK .* sum((U[cMat] * Ke0) .* U[cMat], dims=2)
+    dc  = imfilter(reshape(dc, nely, nelx) ./ dHs, h, "symmetric")
+    dV0 = imfilter(reshape(dV, nely, nelx) ./ dHs, h, "symmetric")
+
+    return reshape(dc, nEl, 1), reshape(dV0, nEl, 1)
+end
+
+function MMAupdate(act, xval::Array{Float64}, low::Array{Float64}, upp::Array{Float64}, 
+        xold1:: Array{Float64}, xold2:: Array{Float64},
+    Obj, Vf:: Float64,
+        dc:: Array{Float64}, dV0:: Array{Float64}, loop:: Int, volfrac:: Float64)
+    move = 0.1
+    ### Initiation of MMA ###
+    m = 1
+    # active design variable
+    n     = length(act)
+    onen  = ones(n, 1)
+    onem  = ones(m, 1)
+    zeron = zeros(n, 1)
+    zerom = zeros(m, 1)
+    a_mma = zerom
+    c_mma = 1.0e3 * onem
+    d_mma = zerom
+    a0    = 1.0
+    # column vector
+    # xval = xval
+      xmin = max.(xval .- move, zeron)
+      xmax = min.(xval .+ move, onen)
+
+    # low = low
+    # upp = upp
+    # objective function   
+    f0val  = Obj
+    df0dx  = dc[act]
+    df0dx2 = 0.0 * df0dx
+    # constraint function
+    fval  = Vf / volfrac - 1.0  # column vector
+    dfdx  = reshape(dV0[act], 1, length(act)) ./ volfrac   # (m * n)
+    dfdx2 = 0.0 * dfdx
+
+    # The MMA subproblem is solved at the point xval: 
+    xmma, ymma, zmma, lam, xsi, eta, mu, zet, s, low, upp = 
+        mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,
+            f0val, df0dx, df0dx2, fval, dfdx, dfdx2, low, upp, a0, a_mma, c_mma, d_mma)
+    return xmma, low, upp
+end
+
+function Visualization(setup::SetUp, x::Array{Float64}, loop::Int)
+    nelx, nely = setup.nelx, setup.nely
+    #    cmap  = cgrad(:Blues_9, rev=false)
+         plot  = heatmap(reshape(x, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none, display_type=:gui)
+    # display(plot)
+    # if mod(loop, 10) = = 0
+    # savefig(plot, "./res/res_$loop.pdf")
+    savefig(plot, "./res/design_$loop.png")
+    # end
+
+    # PLOT FINAL DESIGN
+    # heatmap(1.0 .- x[end:-1:1, :], yaxis=false, xaxis=false, legend=:none,color=:greys, grid=false, border=nothing, aspect_ratio=:equal)
+    nothing
+end
+
+
+function Optimization(setup::SetUp, mat::Mat, load::LoadsSupportsBCs, filter::Filter, ini::Initialization, disfeature::DiscretizationFeature)
+    ch, loop  = ini.ch, ini.loop
+    x , xPhys, xOld    = ini.x, ini.xPhys, ini.xOld
+
+    maxit,       maxchang = setup.maxit, setup.maxchang
+    nely,       nelx     = setup.nely,  setup.nelx
+    eta,       beta,     penalCnt,     betaCnt   = setup.eta, setup.beta, setup.penalCnt, setup.betaCnt
+    volfrac, penal, nEl      = mat.volfrac, mat.penal, disfeature.nEl
+    act    = disfeature.act
+
+    Hs, h, ft = filter.Hs, filter.h, filter.ft
+
+    opt_hist = []
+    vf_hist  = []
+
+    ### Initiation of MMA ###
+    xval  = copy(x[act])
+    xold1 = copy(xval)
+    xold2 = copy(xold1)
+
+    low = copy(xval)
+    upp = copy(low)
+    #########################
+    loop_beta = 0
+    anim = @animate while ch > maxchang && loop < maxit || beta < betaCnt[2]
+        @time begin
+            loop = loop + 1
+            # COMPUTE PHYSICAL DENSITY FIELD 
+                  xTilde = imfilter(reshape(x, nely, nelx), h, "symmetric") ./ Hs
+            xPhys[act]   = copy(xTilde[act])
+            if ft > 1
+                f = (mean(prj(xPhys[act], eta, beta)) .- volfrac) * (ft == 3)
+                while abs(f) > maxchang
+                    eta = eta - f / mean(deta(xPhys[:], eta, beta))
+                    f   = mean(prj(xPhys[act], eta, beta)) - volfrac
+                end
+                filter.dHs = Hs ./ reshape(dprj(xTilde, eta, beta), nely, nelx)
+                xPhys      = prj(xPhys, eta, beta)
+            end
+            ch   = norm(xPhys - xOld) ./ sqrt(nEl)
+            xOld = copy(xPhys)
+            #~ SETUP AND SOLVE EQUILIBRIUM EQUATIONS
+            U, C, Vf = FiniteElementAnalasys(mat, disfeature, load, xPhys)
+            push!(opt_hist, C)
+            push!(vf_hist, Vf)
+            #~ COMPUTE SENSITIVITIES
+            dc, dV0 = SensitivityAnalasys(setup, filter, mat, disfeature, U, xPhys)
+            #~ MMA iteration
+            xmma, low, upp = MMAupdate(act, xval, low, upp, xold1, xold2, C, Vf, dc, dV0, loop, Mat().volfrac)
+            # Some vectors are updated: 
+            xold2 = copy(xold1)
+            xold1 = copy(xval)
+            xval  = copy(xmma)
+
+            x[act] = xval
+
+            #~ CONTINUATION
+            # if  mat.penal == 1.0
+                mat.penal = cnt(mat.penal, penalCnt,loop, ch, maxchang)
+            # end
+            if mat.penal < 3.0
+                beta      = 2.0
+            else
+                loop_beta  = loop_beta + 1
+                beta       = cnt(beta, betaCnt, loop_beta, ch, maxchang)
+                # restart mma
+            end
+
+            heatmap(reshape(xPhys, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none)
+
+        end
+        if mod(loop, 20) == 0
+            println("It.: $loop C.: $C Vf.: $Vf ch.: $ch, p.: $(mat.penal) beta.:$beta eta.: $eta ")
+            Visualization(setup, xPhys, loop)
+        end
+    end
+    # gif(anim, "./top/cantilever.gif", fps=8)
+    return xPhys, opt_hist, vf_hist, anim
+end
+
+# end

top_mma/utils.jl

@@ -0,0 +1,57 @@
+# export loads!, bcs!, passive_domain!
+export prj, deta, dprj, cnt
+export meshgrid
+export Visualization
+
+function prj(v::Array{Float64}, eta::Float64, beta)
+    return (tanh(beta * eta) .+ tanh.(beta * (v[:] .- eta))) ./ (tanh(beta * eta) + tanh(beta * (1 - eta)))
+end
+
+function deta(v::Array{Float64}, eta::Float64, beta)
+    return -beta * csch(beta) .* sech.(beta * (v[:] .- eta)) .^ 2 .* sinh.(v[:] * beta) .* sinh.((1 .- v[:]) * beta)
+end
+
+function dprj(v::Array{Float64}, eta::Float64, beta)
+    return beta * (1 .- tanh.(beta * (v .- eta)) .^ 2) ./ (tanh(beta * eta) + tanh(beta * (1 - eta)))
+end
+
+function cnt(v, vCnt::Array, l::Int, ch::Float64, maxchang::Float64)
+    return v + (l >= vCnt[1]) * (v < vCnt[2]) * (mod(l, vCnt[3]) == 0 || ch <= maxchang) * vCnt[4]
+end
+
+
+"""
+meshgrid(vx)
+Computes an (x,y)-grid from the vectors (vx,vx).
+For more information, see the MATLAB documentation.
+"""
+meshgrid(v::AbstractVector) = meshgrid(v, v)
+
+"""
+meshgrid(vx,vy)
+Computes an (x,y)-grid from the vectors (vx,vy).
+For more information, see the MATLAB documentation.
+"""
+function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T}) where {T}
+    m, n = length(vy), length(vx)
+    vx = reshape(vx, 1, n)
+    vy = reshape(vy, m, 1)
+    (repeat(vx, m, 1), repeat(vy, 1, n))
+end
+
+"""
+meshgrid(vx,vy,vz)
+Computes an (x,y,z)-grid from the vectors (vx,vy,vz).
+For more information, see the MATLAB documentation.
+"""
+function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T},
+    vz::AbstractVector{T}) where {T}
+    m, n, o = length(vy), length(vx), length(vz)
+    vx = reshape(vx, 1, n, 1)
+    vy = reshape(vy, m, 1, 1)
+    vz = reshape(vz, 1, 1, o)
+    om = ones(Int, m)
+    on = ones(Int, n)
+    oo = ones(Int, o)
+    (vx[om, :, oo], vy[:, on, oo], vz[om, on, :])
+end

top_oc/main_test.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "module test\n",
+    "export main\n",
+    "include(\"/Users/yu/TopCodes/Julia/test/top99neo.jl\")\n",
+    "# include(joinpath(@__DIR__, \"top99neo.jl\"))\n",
+    "using .TopOpt99neo\n",
+    "function main()\n",
+    "    setup = SetUp()\n",
+    "    mat = Mat()\n",
+    "    disfeature = DiscretizationFeature(setup, mat)\n",
+    "    load = LoadsSupportsBCs(setup, disfeature)\n",
+    "    ini = Initialization(setup, disfeature, mat)\n",
+    "    filter = Filter(setup)\n",
+    "    xPhys, opt_hist, vf_hist, anim = Optimization(setup, mat, load, filter, ini, disfeature)\n",
+    "    return xPhys, opt_hist, vf_hist, anim\n",
+    "end\n",
+    "\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using .test\n",
+    "\n",
+    "main()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Plots\n",
+    "gif(anim, \"./top/top_hist.gif\", fps=8)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.7.0",
+   "language": "julia",
+   "name": "julia-1.7"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.7.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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top_oc/top99neo.jl

@@ -0,0 +1,346 @@
+__precompile__()
+module TopOpt99neo
+
+using LinearAlgebra, SparseArrays
+using Plots
+# : heatmap, savefig, @animate
+using ImageFiltering: imfilter
+using Statistics: mean
+
+using BenchmarkTools
+
+BLAS.set_num_threads(1)
+
+abstract type top99neo end
+
+include("utils.jl")
+
+export SetUp, Mat, DiscretizationFeature, LoadsSupportsBCs, Initialization, Filter
+export Optimization, Visualization
+
+mutable struct SetUp <: top99neo
+    nelx::Int
+    nely::Int
+
+    eta::Float64
+    beta::Int
+    betaCnt::NTuple{4,Int}
+
+    move::Float64
+    maxit::Int
+    maxchang::Float64
+
+    pasS::Array{Int}
+    pasV::Array{Int}
+
+    function SetUp()
+        nelx = 150
+        nely = 50
+        # volfrac = 0.3
+        maxit = 300
+        move = 0.1
+        beta = 2
+        eta = 0.5
+        maxchang = 1e-6
+        # penalCnt = {maxit+1, 3, 25, 0.25}
+        betaCnt = (2, 32, 10, 2) # continuation scheme on beta parCont = { istart,maxPar,steps,deltaPar }
+
+        pasS, pasV = Array([]), Array([])
+
+        new(nelx, nely, eta, beta, betaCnt, move, maxit, maxchang, pasS, pasV)
+    end
+
+end
+
+mutable struct Mat <: top99neo
+    E0::Float64
+    Emin::Float64
+    ν::Float64
+    penal::Float64
+    volfrac::Float64
+
+    function Mat()
+        E0 = 1
+        Emin = 1e-9
+        ν = 0.3
+        penal = 3.0
+        volfrac = 0.3
+        new(E0, Emin, ν, penal, volfrac)
+    end
+end
+
+struct DiscretizationFeature <: top99neo
+    nEl::Int
+    nodeNrs::Array{Int,2}
+    nDof::Int
+    act
+
+    cMat::Array{Int}
+    Iar::Array{Int}
+    Ke::Array{Float64,1}
+    Ke0::Array{Float64,2}
+
+    function DiscretizationFeature(setup::SetUp, mat::Mat)
+        nelx = setup.nelx
+        nely = setup.nely
+        pasS, pasV = setup.pasS, setup.pasV
+        ν = mat.ν
+
+        nEl = nely * nelx
+        nodeNrs = reshape(1:(1+nelx)*(1+nely), nely + 1, nelx + 1)
+        nDof = (nely + 1) * (nelx + 1) * 2
+        act = setdiff(collect(1:nEl), union(pasS, pasV))
+
+        cVec = reshape(2 * nodeNrs[1:end-1, 1:end-1] .+ 1, nEl, 1)
+        cMat = Int.(repeat(cVec, 1, 8) + repeat([0 1 2 * nely .+ [2 3 0 1] -2 -1], nelx * nely, 1))
+
+        FuckRow = [1 2 3 4 5 6 7 8]
+        sI::Array{Int}, sII::Array{Int} = copy(FuckRow), fill(1, 1, 8)
+        @inbounds for j in 2:8
+            sI = cat(sI, FuckRow[j:8]'; dims=2)
+            # sII = cat(2, sII, repmat(j, 1, 8 - j + 1))
+            sII = cat(sII, fill(j, 1, 8 - j + 1); dims=2)
+        end
+        iK::Array{Int,2}, jK::Array{Int,2} = cMat[:, sI][:, 1, :]', cMat[:, sII][:, 1, :]'
+        Iar = sort([iK[:] jK[:]]; dims=2, rev=true) # comma is a newline
+        # iK, jK .= 0 , 0
+        c1 = [12, 3, -6, -3, -6, -3, 0, 3, 12, 3, 0, -3, -6, -3, -6, 12, -3, 0, -3, -6, 3, 12, 3, -6, 3, -6, 12, 3, -6, -3, 12, 3, 0, 12, -3, 12]
+        c2 = [-4, 3, -2, 9, 2, -3, 4, -9, -4, -9, 4, -3, 2, 9, -2, -4, -3, 4, 9, 2, 3, -4, -9, -2, 3, 2, -4, 3, -2, 9, -4, -9, 4, -4, -3, -4]
+        Ke = 1 / (1 - ν^2) / 24 .* (c1 .+ ν .* c2) # half-KE vector
+        # full KE
+        # Ke0::Array{Float64} = zeros(8, 8)
+        # start_id, end_id = 1, 8
+        # for i in 1:8
+        #     Ke0[i:8, i] = Ke[start_id:end_id]
+        #     start_id, end_id = end_id + 1, 2 * end_id - start_id
+        # end
+
+        Ke0::Array{Float64} = zeros(8, 8)
+        # Index::Array{Int} = [sI' sII']
+        # Ke0[sI, sII] = Ke
+        Index = findall(isequal(1), tril(ones(8, 8)))
+        Ke0[Index] = Ke'
+        # Ke0 = reshape(Ke0, 8, 8)
+        Ke0 = Ke0 + Ke0' - diagm(diag(Ke0))
+        new(nEl, nodeNrs, nDof, act, cMat, Iar, Ke, Ke0)
+    end
+end
+
+mutable struct LoadsSupportsBCs <: top99neo
+    lcDof::Array{Int}
+    F
+    free::Array{Int}
+    fixed::Array{Int}
+
+    function LoadsSupportsBCs(setup::SetUp, disfeature::DiscretizationFeature)
+        nelx = setup.nelx
+        nely = setup.nely
+        nodeNrs = disfeature.nodeNrs
+        nDof = disfeature.nDof
+
+        load_position::Symbol = :half_MBB
+        if load_position == :half_MBB
+            load_nodey, load_nodex = 1, 1
+            # fixed = union([1:2:2*(nely+1)], 2 * nodeNrs[nely+1, nelx+1])
+            fixed = union(collect(1:2:2*(nely+1)), 2 * nodeNrs[end, end])
+        elseif load_position == :cantilever
+            load_nodey = nely + 1
+            load_nodex = nelx / 2 + 1
+            fixed = 1:2*(nely+1)
+        end
+
+        F = spzeros(nDof)
+
+        load_type::Symbol = :pin
+        if load_type == :pin # 1 point
+            lcDof = collect(2 * nodeNrs[load_nodey, load_nodex])
+            F[2] = -1.0
+        elseif load_type == :points # 5 points
+            # lcDof = collect(2 * nodeNrs[load_nodey, load_nodex], nodeNrs[load_nodey, load_nodex-1], nodeNrs[load_nodey, load_nodex-2], nodeNrs[load_nodey, load_nodex+1], nodeNrs[load_nodey, load_nodex+2])
+            F[lcDof'] .= -1.0
+        elseif load_type == :line
+            lcDof = [2:2*(nely+1):nDof]
+            F = spzeros(nDof, 1)
+            F[lcDof'] .= -1.0
+            # F = sparse(lcDof', ones(length(lcDof')), -1.0)
+        end
+        all = collect(1:nDof)
+        free = setdiff(all, fixed)
+
+        new(lcDof, F, free, fixed)
+    end
+end
+
+struct Initialization <: top99neo
+    x::Array{Float64}
+    xPhys::Array{Float64}
+    xOld::Array{Float64}
+    ch::Float64
+    loop::Int
+
+    function Initialization(setup::SetUp, disfeature::DiscretizationFeature, mat::Mat)
+        pasV = setup.pasV
+        pasS = setup.pasS
+        act = disfeature.act
+        volfrac = mat.volfrac
+        nEl = disfeature.nEl
+        # nDof = disfeature.nDof
+        # column vectors
+        x = zeros(nEl, 1)
+        # x[act] .= (volfrac * (nEl - length(pasV)) - length(pasS)) / length(act)
+        x[act] .= volfrac 
+        x[pasS] .= 1.0
+        xPhys, xOld, ch, loop = copy(x), ones(nEl, 1), 1.0, 0
+        # x̅  x̃
+    
+        new(x, xPhys, xOld, ch, loop)
+    end
+end
+
+mutable struct Filter <: top99neo
+    rmin::Float64
+    ft::Int
+    h::Array{Float64}
+    Hs::Array{Float64}
+    dHs::Array{Float64}
+
+    function Filter(setup::SetUp)
+        nelx = setup.nelx
+        nely = setup.nely
+        # volfrac = mat.volfrac
+        # bcF = setup.bcF
+        rmin = nely / 20
+        ft = 3 # 1 density 2 projection 3 volume preserving
+        dy, dx = meshgrid(-ceil(rmin)+1:ceil(rmin)-1, -ceil(rmin)+1:ceil(rmin)-1)
+        h = max.(0, rmin .- sqrt.(dx .^ 2 + dy .^ 2))
+        Hs = imfilter(ones(nely, nelx), h, "symmetric")
+        dHs = Hs
+
+        new(rmin, ft, h, Hs, dHs)
+    end
+end
+
+
+function FiniteElementAnalasys(mat::Mat, disfeature::DiscretizationFeature, load::LoadsSupportsBCs, xPhys::Array{Float64})
+    nEl, nDof, Iar, Ke = disfeature.nEl, disfeature.nDof, disfeature.Iar, disfeature.Ke
+    Emin, penal, E0 = mat.Emin, mat.penal, mat.E0
+    F, free = load.F, load.free
+
+    sK = Emin .+ xPhys .^ penal .* (E0 - Emin)
+    sK = reshape(Ke[:] * sK', length(Ke) * nEl, 1)
+    K::SparseMatrixCSC = sparse(Iar[:, 1], Iar[:, 2], vec(sK), nDof, nDof)
+    U = zeros(nDof)
+    # U[free] = cholesky(Symmetric(K[free, free], :L), check=false) \ F[free
+    U[free] = cholesky(Symmetric(K, :L)[free, free], check=false) \ F[free]
+    Obj = F' * U
+    Vf = mean(xPhys)
+    return U, Obj, Vf
+end
+
+function SensitivityAnalasys(setup::SetUp, filter::Filter, mat::Mat, disfeature::DiscretizationFeature, U::Array{Float64}, xPhys::Array{Float64})
+    nelx, nely, act = setup.nelx, setup.nely, disfeature.act
+    dHs, h = filter.dHs, filter.h
+    E0, Emin, volfrac, penal = mat.E0, mat.Emin, mat.volfrac, mat.penal
+    nEl, cMat, Ke0 = disfeature.nEl, disfeature.cMat, disfeature.Ke0
+
+    dsK, dV = zeros(nEl, 1), zeros(nEl, 1)
+    #! active volume fraction in the design domain 
+    # fval = sum(xPhys[act])./length( act )./volfrac.-1.0
+    dV[act] .= 1.0 / length(act) / volfrac
+    dsK[act] = -penal * (E0 - Emin) .* xPhys[act] .^ (penal - 1)
+
+    dc = dsK .* sum((U[cMat] * Ke0) .* U[cMat], dims=2)
+    dc = imfilter(reshape(dc, nely, nelx) ./ dHs, h, "symmetric")
+    dV0 = imfilter(reshape(dV, nely, nelx) ./ dHs, h, "symmetric")
+
+    return dc, dV0
+end
+
+#  UPDATE DESIGN VARIABLES AND APPLY CONTINUATION
+function OCupdate(x::Array{Float64}, setup::SetUp, mat::Mat, disfeature::DiscretizationFeature, dc::Array{Float64}, dV0::Array{Float64})
+    act, move, volfrac = disfeature.act, setup.move, mat.volfrac
+    xT = zeros(length(act), 1)
+    xT = x[act]
+    xU, xL = xT .+ move, xT .- move
+    ocP = xT .* real.(sqrt.(-dc[act] ./ dV0[act]))
+    l = [0 mean(ocP) / volfrac]
+    while (l[2] - l[1]) / (l[2] + l[1]) > 1e-4
+        lmid = 0.5 * (l[1] + l[2])
+        x[act] = max.(max.(min.(min.(ocP ./ lmid, xU), 1.0), xL), 0.0)
+        if mean(x) > volfrac
+            l[1] = lmid
+        else
+            l[2] = lmid
+        end
+    end
+    return x
+end
+
+function Visualization(setup::SetUp, x::Array{Float64}, loop::Int)
+    nelx, nely = setup.nelx, setup.nely
+    # cmap = cgrad(:Blues_9, rev=false)
+    plot = heatmap(reshape(x, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none, display_type=:gui)
+    # display(plot)
+    # if mod(loop, 10) == 0
+    savefig(plot, "./top/res_$loop.pdf")
+    # end
+
+    # PLOT FINAL DESIGN
+    # heatmap(1.0 .- x[end:-1:1, :], yaxis=false, xaxis=false, legend=:none,color=:greys, grid=false, border=nothing, aspect_ratio=:equal)
+    nothing
+end
+function Optimization(setup::SetUp, mat::Mat, load::LoadsSupportsBCs, filter::Filter, ini::Initialization, disfeature::DiscretizationFeature)
+    ch, loop = ini.ch, ini.loop
+    x, xPhys, xOld = ini.x, ini.xPhys, ini.xOld
+
+    maxit, maxchang = setup.maxit, setup.maxchang
+    nely, nelx = setup.nely, setup.nelx
+    eta, beta, betaCnt = setup.eta, setup.beta, setup.betaCnt
+    volfrac, penal, nEl = mat.volfrac, mat.penal, disfeature.nEl
+    act = disfeature.act
+
+    Hs, h, ft = filter.Hs, filter.h, filter.ft
+
+    opt_hist = []
+    vf_hist = []
+
+    anim = @animate while ch > maxchang && loop < maxit || beta < betaCnt[2]
+        @time begin
+            loop = loop + 1
+            # COMPUTE PHYSICAL DENSITY FIELD 
+            xTilde = imfilter(reshape(x, nely, nelx), h, "symmetric") ./ Hs
+            xPhys[act] = copy(xTilde[act])
+            if ft > 1
+                f = (mean(prj(xPhys, eta, beta)) .- volfrac) * (ft == 3)
+                while abs(f) > maxchang
+                    eta = eta - f / mean(deta(xPhys[:], eta, beta))
+                    f = mean(prj(xPhys, eta, beta)) - volfrac
+                end
+                filter.dHs = Hs ./ reshape(dprj(xTilde, eta, beta), nely, nelx)
+                xPhys = prj(xPhys, eta, beta)
+            end
+            ch = norm(xPhys - xOld) ./ sqrt(nEl)
+            xOld = copy(xPhys)
+            # SETUP AND SOLVE EQUILIBRIUM EQUATIONS
+            U, C, Vf = FiniteElementAnalasys(mat, disfeature, load, xPhys)
+            push!(opt_hist, C)
+            push!(vf_hist, Vf)
+            # COMPUTE SENSITIVITIES
+            dc, dV0 = SensitivityAnalasys(setup, filter, mat, disfeature, U, xPhys)
+            # OC iteration
+            x = OCupdate(x, setup, mat, disfeature, dc, dV0)
+            # CONTINUATION
+            beta = cnt(beta, betaCnt, loop, ch, maxchang)
+
+            heatmap(reshape(xPhys, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none)
+
+        end
+        if mod(loop, 10) == 0
+            println("It.: $loop C.: $C Vf.: $Vf ch.: $ch, p.: $penal beta.:$beta eta.: $eta ")
+            Visualization(setup, xPhys, loop)
+        end
+    end
+    return xPhys, opt_hist, vf_hist, anim
+end
+
+end

top_oc/utils.jl

@@ -0,0 +1,71 @@
+# export loads!, bcs!, passive_domain!
+export prj, deta, dprj, cnt
+export meshgrid
+export Visualization
+
+function prj(v::Array{Float64}, eta::Float64, beta::Int)
+    return (tanh(beta * eta) .+ tanh.(beta * (v[:] .- eta))) ./ (tanh(beta * eta) + tanh(beta * (1 - eta)))
+end
+
+function deta(v::Array{Float64}, eta::Float64, beta::Int)
+    return -beta * csch(beta) .* sech.(beta * (v[:] .- eta)) .^ 2 .* sinh.(v[:] * beta) .* sinh.((1 .- v[:]) * beta)
+end
+
+function dprj(v::Array{Float64}, eta::Float64, beta::Int)
+    return beta * (1 .- tanh.(beta * (v .- eta)) .^ 2) ./ (tanh(beta * eta) + tanh(beta * (1 - eta)))
+end
+
+function cnt(v::Int, vCnt::NTuple{4,Int}, l::Int, ch::Float64, maxchang::Float64)
+    return v + (l >= vCnt[1]) * (v < vCnt[2]) * (mod(l, vCnt[3]) == 0 || ch <= maxchang) * vCnt[4]
+end
+
+
+"""
+meshgrid(vx)
+Computes an (x,y)-grid from the vectors (vx,vx).
+For more information, see the MATLAB documentation.
+"""
+meshgrid(v::AbstractVector) = meshgrid(v, v)
+
+"""
+meshgrid(vx,vy)
+Computes an (x,y)-grid from the vectors (vx,vy).
+For more information, see the MATLAB documentation.
+"""
+function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T}) where {T}
+    m, n = length(vy), length(vx)
+    vx = reshape(vx, 1, n)
+    vy = reshape(vy, m, 1)
+    (repeat(vx, m, 1), repeat(vy, 1, n))
+end
+
+"""
+meshgrid(vx,vy,vz)
+Computes an (x,y,z)-grid from the vectors (vx,vy,vz).
+For more information, see the MATLAB documentation.
+"""
+function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T},
+    vz::AbstractVector{T}) where {T}
+    m, n, o = length(vy), length(vx), length(vz)
+    vx = reshape(vx, 1, n, 1)
+    vy = reshape(vy, m, 1, 1)
+    vz = reshape(vz, 1, 1, o)
+    om = ones(Int, m)
+    on = ones(Int, n)
+    oo = ones(Int, o)
+    (vx[om, :, oo], vy[:, on, oo], vz[om, on, :])
+end
+
+function Visualization(setup::SetUp, x::Array{Float64}, loop::Int)
+    nelx, nely = setup.nelx, setup.nely
+    # cmap = cgrad(:Blues_9, rev=false)
+    plot = heatmap(reshape(x, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none, display_type=:gui)
+    # display(plot)
+    # if mod(loop, 10) == 0
+    savefig(plot, "./top_mma/res_$loop.pdf")
+    # end
+
+    # PLOT FINAL DESIGN
+    # heatmap(1.0 .- x[end:-1:1, :], yaxis=false, xaxis=false, legend=:none,color=:greys, grid=false, border=nothing, aspect_ratio=:equal)
+    nothing
+end