Algorithm to (try to) compute a maximum cut of a graph, via SDP approach. function x = maxcut(L) function [x, cutvalue, cutvalue_upperbound, Y] = maxcut(L, r) L is the Laplacian matrix describing the graph to cut. The Laplacian of a graph is L = D - A, where D is the diagonal degree matrix (D(i, i) is the sum of the weights of the edges adjacent to node i) and A is the symmetric adjacency matrix of the graph (A(i, j) = A(j, i) is the weight of the edge joining nodes i and j). If L is sparse, this will be exploited. If the graph has n nodes, then L is nxn and the output x is a vector of length n such that x(i) is +1 or -1. This partitions the nodes of the graph in two classes, in an attempt to maximize the sum of the weights of the edges that go from one class to the other (MAX CUT problem). cutvalue is the sum of the weights of the edges 'cut' by the partition x. If the algorithm reached the global optimum of the underlying SDP problem, then it produces an upperbound on the maximum cut value. This value is returned in cutvalue_upperbound if it is found. Otherwise, that output is set to NaN. If r is specified (by default, r = n), the algorithm will stop at rank r. This may prevent the algorithm from reaching a globally optimal solution for the underlying SDP problem (but can greatly help in keeping the execution time under control). If a global optimum of the SDP is reached before rank r, the algorithm will stop of course. Y is a matrix of size nxp, with p <= r, such that X = Y*Y' is the best solution found for the underlying SDP problem. If cutvalue_upperbound is not NaN, then Y*Y' is optimal for the SDP and cutvalue_upperbound is its cut value. By Goemans and Williamson 1995, it is known that if the optimal value of the SDP is reached, then the returned cut, in expectation, is at most at a fraction 0.878 of the optimal cut. (This is not exactly valid because we do not use random projection here; sign(Y*randn(size(Y, 2), 1)) will give a cut that respects this statement -- it's usually worse though). The algorithm is essentially that of: Journee, Bach, Absil and Sepulchre, SIAM 2010 Low-rank optimization on the cone of positive semidefinite matrices. It is itself based on the famous SDP relaxation of MAX CUT: Goemans and Williamson, 1995 Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. See also: elliptope_SDP

- elliptopefactory Manifold of n-by-n psd matrices of rank k with unit diagonal elements.
- trustregions Riemannian trust-regions solver for optimization on manifolds.
- statsfunhelper Helper tool to create a statsfun for the options structure of solvers.

- function [Y, info] = maxcut_fixedrank(L, Y)
- function store = prepare(Y, store)
- function [f, store] = cost(Y, store)
- function [g, store] = egrad(Y, store)
- function [h, store] = ehess(Y, U, store)

0001 function [x, cutvalue, cutvalue_upperbound, Y] = maxcut(L, r) 0002 % Algorithm to (try to) compute a maximum cut of a graph, via SDP approach. 0003 % 0004 % function x = maxcut(L) 0005 % function [x, cutvalue, cutvalue_upperbound, Y] = maxcut(L, r) 0006 % 0007 % L is the Laplacian matrix describing the graph to cut. The Laplacian of a 0008 % graph is L = D - A, where D is the diagonal degree matrix (D(i, i) is the 0009 % sum of the weights of the edges adjacent to node i) and A is the 0010 % symmetric adjacency matrix of the graph (A(i, j) = A(j, i) is the weight 0011 % of the edge joining nodes i and j). If L is sparse, this will be 0012 % exploited. 0013 % 0014 % If the graph has n nodes, then L is nxn and the output x is a vector of 0015 % length n such that x(i) is +1 or -1. This partitions the nodes of the 0016 % graph in two classes, in an attempt to maximize the sum of the weights of 0017 % the edges that go from one class to the other (MAX CUT problem). 0018 % 0019 % cutvalue is the sum of the weights of the edges 'cut' by the partition x. 0020 % 0021 % If the algorithm reached the global optimum of the underlying SDP 0022 % problem, then it produces an upperbound on the maximum cut value. This 0023 % value is returned in cutvalue_upperbound if it is found. Otherwise, that 0024 % output is set to NaN. 0025 % 0026 % If r is specified (by default, r = n), the algorithm will stop at rank r. 0027 % This may prevent the algorithm from reaching a globally optimal solution 0028 % for the underlying SDP problem (but can greatly help in keeping the 0029 % execution time under control). If a global optimum of the SDP is reached 0030 % before rank r, the algorithm will stop of course. 0031 % 0032 % Y is a matrix of size nxp, with p <= r, such that X = Y*Y' is the best 0033 % solution found for the underlying SDP problem. If cutvalue_upperbound is 0034 % not NaN, then Y*Y' is optimal for the SDP and cutvalue_upperbound is its 0035 % cut value. 0036 % 0037 % By Goemans and Williamson 1995, it is known that if the optimal value of 0038 % the SDP is reached, then the returned cut, in expectation, is at most at 0039 % a fraction 0.878 of the optimal cut. (This is not exactly valid because 0040 % we do not use random projection here; sign(Y*randn(size(Y, 2), 1)) will 0041 % give a cut that respects this statement -- it's usually worse though). 0042 % 0043 % The algorithm is essentially that of: 0044 % Journee, Bach, Absil and Sepulchre, SIAM 2010 0045 % Low-rank optimization on the cone of positive semidefinite matrices. 0046 % 0047 % It is itself based on the famous SDP relaxation of MAX CUT: 0048 % Goemans and Williamson, 1995 0049 % Improved approximation algorithms for maximum cut and satisfiability 0050 % problems using semidefinite programming. 0051 % 0052 % See also: elliptope_SDP 0053 0054 % This file is part of Manopt: www.manopt.org. 0055 % Original author: Nicolas Boumal, July 18, 2013 0056 % Contributors: 0057 % Change log: 0058 % 0059 % April 3, 2015 (NB): 0060 % L products now counted with the new shared memory system. This is 0061 % more reliable and more flexible than using a global variable. 0062 % Aug 20, 2021 (XJ): 0063 % Added AD to compute the egrad and the ehess 0064 0065 % If no inputs are provided, generate a random graph Laplacian. 0066 % This is for illustration purposes only. 0067 if ~exist('L', 'var') || isempty(L) 0068 n = 20; 0069 A = triu(rand(n) <= .4, 1); 0070 A = A+A'; 0071 D = diag(sum(A, 2)); 0072 L = D-A; 0073 end 0074 0075 0076 n = size(L, 1); 0077 assert(size(L, 2) == n, 'L must be square.'); 0078 0079 if ~exist('r', 'var') || isempty(r) || r > n 0080 r = n; 0081 end 0082 0083 % We will let the rank increase. Each rank value will generate a cut. 0084 % We have to go up in the rank to eventually find a certificate of SDP 0085 % optimality. This in turn will provide an upperbound on the MAX CUT 0086 % value and ensure that we're doing well, according to Goemans and 0087 % Williamson's argument. In practice though, the good cuts often come 0088 % up for low rank values, so we better keep track of the best one. 0089 best_x = ones(n, 1); 0090 best_cutvalue = 0; 0091 cutvalue_upperbound = NaN; 0092 0093 time = []; 0094 cost = []; 0095 0096 for rr = 2 : r 0097 0098 manifold = elliptopefactory(n, rr); 0099 0100 if rr == 2 0101 0102 % At first, for rank 2, generate a random point. 0103 Y0 = manifold.rand(); 0104 0105 else 0106 0107 % To increase the rank, we could just add a column of zeros to 0108 % the Y matrix. Unfortunately, this lands us in a saddle point. 0109 % To escape from the saddle, we may compute an eigenvector of 0110 % Sy associated to a negative eigenvalue: that will yield a 0111 % (second order) descent direction Z. See Journee et al ; Sy is 0112 % linked to dual certificates for the SDP. 0113 Y0 = [Y zeros(n, 1)]; 0114 LY0 = L*Y0; 0115 Dy = spdiags(sum(LY0.*Y0, 2), 0, n, n); 0116 Sy = (Dy - L)/4; 0117 % Find the smallest (the "most negative") eigenvalue of Sy. 0118 eigsopts.isreal = true; 0119 [v, s] = eigs(Sy, 1, 'SA', eigsopts); 0120 % If there is no negative eigenvalue for Sy, than we are not at 0121 % a saddle point: we're actually done! 0122 if s >= -1e-8 0123 % We can stop here: we found the global optimum of the SDP, 0124 % and hence the reached cost is a valid upper bound on the 0125 % maximum cut value. 0126 cutvalue_upperbound = max(-[info.cost]); 0127 break; 0128 end 0129 0130 % This is our escape direction. 0131 Z = manifold.proj(Y0, [zeros(n, rr-1) v]); 0132 0133 % % These instructions can be uncommented to see what the cost 0134 % % function looks like at a saddle point. But will require the 0135 % % problem structure which is not defined here: see the helper 0136 % % function. 0137 % plotprofile(problem, Y0, Z, linspace(-1, 1, 101)); 0138 % drawnow; pause; 0139 0140 % Now make a step in the Z direction to escape from the saddle. 0141 % It is not obvious that it is ok to do a unit step ... perhaps 0142 % need to be cautious here with the stepsize. It's not too 0143 % critical though: the important point is to leave the saddle 0144 % point. But it's nice to guarantee monotone decrease of the 0145 % cost, and we can't do that with a constant step (at least, 0146 % not without a proper argument to back it up). 0147 stepsize = 1; 0148 Y0 = manifold.retr(Y0, Z, stepsize); 0149 0150 end 0151 0152 % Use the Riemannian optimization based algorithm lower in this 0153 % file to reach a critical point (typically a local optimizer) of 0154 % the max cut cost with fixed rank, starting from Y0. 0155 [Y, info] = maxcut_fixedrank(L, Y0); 0156 0157 % Some info logging. 0158 thistime = [info.time]; 0159 if ~isempty(time) 0160 thistime = time(end) + thistime; 0161 end 0162 time = [time thistime]; %#ok<AGROW> 0163 cost = [cost [info.cost]]; %#ok<AGROW> 0164 0165 % Time to turn the matrix Y into a cut. 0166 % We can either do the random rounding as follows: 0167 % x = sign(Y*randn(rr, 1)); 0168 % or extract the "PCA direction" of the points in Y and cut 0169 % orthogonally to that direction, as follows (seems faster than 0170 % calling svds): 0171 [U, ~, ~] = svd(Y, 0); 0172 u = U(:, 1); 0173 x = sign(u); 0174 0175 cutvalue = (x'*L*x)/4; 0176 if cutvalue > best_cutvalue 0177 best_x = x; 0178 best_cutvalue = cutvalue; 0179 end 0180 0181 end 0182 0183 x = best_x; 0184 cutvalue = best_cutvalue; 0185 0186 figure; 0187 plot(time, -cost, '.-'); 0188 xlabel('Time [s]'); 0189 ylabel('Relaxed cut value'); 0190 title('The relaxed cut value is an upper bound on the optimal cut value.'); 0191 0192 end 0193 0194 0195 function [Y, info] = maxcut_fixedrank(L, Y) 0196 % Try to solve the (fixed) rank r relaxed max cut program, based on the 0197 % Laplacian of the graph L and an initial guess Y. L is nxn and Y is nxr. 0198 0199 [n, r] = size(Y); 0200 assert(all(size(L) == n)); 0201 0202 % The fixed rank elliptope geometry describes symmetric, positive 0203 % semidefinite matrices of size n with rank r and all diagonal entries 0204 % are 1. 0205 manifold = elliptopefactory(n, r); 0206 0207 % % If you want to compare the performance of the elliptope geometry 0208 % % against the (conceptually simpler) oblique manifold geometry, 0209 % % uncomment this line. 0210 % manifold = obliquefactory(r, n, true); 0211 0212 problem.M = manifold; 0213 0214 % % For rapid prototyping, these lines suffice to describe the cost 0215 % % function and its gradient and Hessian (here expressed using the 0216 % % Euclidean gradient and Hessian). 0217 % problem.cost = @(Y) -trace(Y'*L*Y)/4; 0218 % problem.egrad = @(Y) -(L*Y)/2; 0219 % problem.ehess = @(Y, U) -(L*U)/2; 0220 0221 % It's also possible to compute the egrad and the ehess via AD but is 0222 % much slower. Notice that the function trace is not supported for AD 0223 % so far. Replace it with ctrace described in the file manoptADhelp.m. 0224 % problem.cost = @(Y) -ctrace(Y'*L*Y)/4; 0225 % problem = manoptAD(problem); 0226 0227 % Instead of the prototyping version, the functions below describe the 0228 % cost, gradient and Hessian using the caching system (the store 0229 % structure). This alows to execute exactly the required number of 0230 % multiplications with the matrix L. These multiplications are counted 0231 % using the shared memory in the store structure: that memory is 0232 % shared , so we get access to the same data, regardless of the 0233 % point Y currently visited. 0234 0235 % For every visited point Y, we will need L*Y. This function makes sure 0236 % the quantity L*Y is available, but only computes it if it wasn't 0237 % already computed. 0238 function store = prepare(Y, store) 0239 if ~isfield(store, 'LY') 0240 % Compute and store the product for the current point Y. 0241 store.LY = L*Y; 0242 % Create / increment the shared counter (independent of Y). 0243 if isfield(store.shared, 'counter') 0244 store.shared.counter = store.shared.counter + 1; 0245 else 0246 store.shared.counter = 1; 0247 end 0248 end 0249 end 0250 0251 problem.cost = @cost; 0252 function [f, store] = cost(Y, store) 0253 store = prepare(Y, store); 0254 LY = store.LY; 0255 f = -(Y(:)'*LY(:))/4; % = -trace(Y'*LY)/4; but faster 0256 end 0257 0258 problem.egrad = @egrad; 0259 function [g, store] = egrad(Y, store) 0260 store = prepare(Y, store); 0261 LY = store.LY; 0262 g = -LY/2; 0263 end 0264 0265 problem.ehess = @ehess; 0266 function [h, store] = ehess(Y, U, store) 0267 store = prepare(Y, store); % this line is not strictly necessary 0268 LU = L*U; 0269 store.shared.counter = store.shared.counter + 1; 0270 h = -LU/2; 0271 end 0272 0273 % statsfun is called exactly once after each iteration (including after 0274 % the evaluation of the cost at the initial guess). We then register 0275 % the value of the L-products counter (which counts how many products 0276 % were needed so far). 0277 % options.statsfun = @statsfun; 0278 % function stats = statsfun(problem, Y, stats, store) %#ok 0279 % stats.Lproducts = store.shared.counter; 0280 % end 0281 % Equivalent, but simpler syntax: 0282 options.statsfun = statsfunhelper('Lproducts', ... 0283 @(problem, Y, stats, store) store.shared.counter ); 0284 0285 0286 % % Diagnostics tools: to make sure the gradient and Hessian are 0287 % % correct during the prototyping stage. 0288 % checkgradient(problem); pause; 0289 % checkhessian(problem); pause; 0290 0291 % % To investigate the effect of the rotational invariance when using 0292 % % the oblique or the elliptope geometry, or to study the saddle point 0293 % % issue mentioned above, it is sometimes interesting to look at the 0294 % % spectrum of the Hessian. For large dimensions, this is slow! 0295 % stairs(sort(hessianspectrum(problem, Y))); 0296 % drawnow; pause; 0297 0298 0299 % % When facing a saddle point issue as described in the master 0300 % % function, and when no sure mechanism exists to find an escape 0301 % % direction, it may be helpful to set useRand to true and raise 0302 % % miniter to more than 1, when using trustregions. This will tell the 0303 % % solver to not stop before at least miniter iterations were 0304 % % accomplished (thus disregarding the zero gradient at the saddle 0305 % % point) and to use random search directions to kick start the inner 0306 % % solve (tCG) step. It is not as efficient as finding a sure escape 0307 % % direction, but sometimes it's the best we have. 0308 % options.useRand = true; 0309 % options.miniter = 5; 0310 0311 options.verbosity = 2; 0312 [Y, Ycost, info] = trustregions(problem, Y, options); %#ok<ASGLU> 0313 0314 fprintf('Products with L: %d\n', max([info.Lproducts])); 0315 0316 end

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