Some problems in Mosek when solving integer programming

TomK · July 19, 2020, 6:21am

I use the mosek solver to solve a integer programming problem, there are about 300+ integer variables. It takes several hours to slove but still have about 30% rea gap. Meanwhile I notice that my computer RAM is close to 100% Occupied, then my Matlab stucks.

I notice that when the mosek works, the Occupancy rate of the computer RAM grows. I want to know if it means that my problem cannot be solved when the RAM is used up?

Michal_Adamaszek · July 19, 2020, 6:57am

It is certainly possible that it eats up your RAM. We don’t know what problem you are solving so it is hard to say something specific. If you use big-M make it as tight as possible and don’t use 10^9 or the like.

Can you post the log output (well, if it is very long then at least the beginning up to a few iterations and then the few last iterations)?

TomK · July 19, 2020, 7:33am

Here is the beginning:

Calling Mosek 8.0.0.60: 2952 variables, 2216 equality constraints

MOSEK Version 8.0.0.60 (Build date: 2017-3-1 13:09:33)
Copyright © MOSEK ApS, Denmark. WWW: mosek.com
Platform: Windows/64-X86

Problem
Name :
Objective sense : min
Type : LO (linear optimization problem)
Constraints : 2216
Cones : 0
Scalar variables : 2952
Matrix variables : 0
Integer variables : 720

Optimizer started.
Mixed integer optimizer started.
Threads used: 4
Presolve started.
Presolve terminated. Time = 0.03
Presolved problem: Presolved problem: Clique table size: 20
BRANCHES RELAXS ACT_NDS DEPTH 0 1 0 0 1 0 Cut generation started.
0 2 0 Cut generation 0 3 0 15 18 1 31 34 1 63 66 1 127 130 1 247 250 1 369 371 1 484 485 1 577 578 1 670 669 1 780 779 1 902 898 1 1010 1005 1 1025 1020 16 3 1041 1036 32 5 1073 1068 62 1131 1125 90 7 1195 1188 124 16 1271 1263 156 11 1335 1327 192 20 1404 1396 231 36 1466 1458 275 23 1537 1528 304 11 1598 1588 357 15 1662 1650 401 24 1750 1736 443 13 1818 1803 485 26 1884 1868 533 32 1980 1962 585 22 2058 2038 617 29 2154 2128 655 19 2229 2201 704 23 2284 2254 749 48 2368 2337 771 19 2437 2402 816 43 2488 2453 867 43 2579 2537 908 62 2668 2625 935 52 2726 2683 993 19 2767 2723 1032 72 2840 2795 1067 43 2909 2864 1110 34 2960 2914 1149 45 3032 2983 1195 40 3113 3060 1236 59 3223 3169 1256 75 3359 3301 1276 94 3442 3382 1301 30 3490 3430 1349 29 3549 3489 1378 27 3635 3573 1422 27 3733 3668 1460 47 3830 3758 1481 68 3931 3859 1512 87 4027 3953 1542 31 4110 4034 1589 37 4190 4112 1641 31 4238 4160 1689 30 4297 4217 1728 37 4376 4287 1775 47 4449 4358 1808 15 4521 4427 1852 41 4601 4502 1904 36 4662 4563 1935 53 4756 4652 1973 70 4842 4735 2011 16 4895 4788 2064 30 4932 4824 2099 24 4985 4874 2136 22 5041 4927 2164 44 5119 5002 2220 36 5196 5079 2263 27 5296 5178 2295 32 5373 5254 2330 21 5464 5343 2369 47 5561 5435 2398 64 5628 5502 2435 34 5683 5557 2480 36 5745 5619 2520 30 5800 5673 2561 16 5855 5727 2610 16 5911 5780 2652 30 6002 5866 2691 53 6050 5914 2731 9 6117 5980 2760 21 6204 6065 2799 39 6252 6113 2833 27 6301 6159 2870 35 6359 6215 2898 20 6432 6285 2925 21 6475 6326 2964 39 6528 6377 3005 32 6624 6470 3043 42 6720 6563 3063 32 6756 6598 3097 31 6796 6638 3137 31 6847 6689 3188 20 6898 6740 3221 56 6988 6829 3245 77 7080 6919 3281 43 7095 6934 3281 43 7111 6950 3281 43 7143 6982 3281 43 7207 7046 3281 43 7325 7164 3281 43 7432 7271 3281 43 7556 7395 3281 43 7674 7512 3281 43 7796 7632 3281 43 7911 7747 3281 43 8032 7868 3281 43 8151 7987 3281 43 8217 8050 3323 38 8258 8090 3362 20 8329 8159 3399 72 8376 8205 3440 17 8443 8271 3479 37 8540 8368 3506 19 8623 8450 3543 24 8684 8510 3588 34 8724 8549 3626 12 8778 8602 3658 37 8876 8696 3702 28 8945 8763 3725 24 9019 8831 3755 24 9080 8891 3794 9 9134 8944 3846 45 9204 9012 3884 52 9285 9091 3909 34 9387 9188 3931 57 9460 9258 3966 25 9500 9297 3992 23 9542 9338 4032 11 9617 9408 4077 34 9656 9446 4110 13 9715 9505 4163 50 9787 9576 4201 45 9872 9659 4238 41 9970 9753 4266 29 10028 9809 4300 42 10076 9856 4338 43 10135 9914 4385 55 10183 9961 4431 29 10276 10051 4468 49 10369 10142 4507 20 10431 10204 4569 33 10511 10282 4621 44 10608 10378 4648 23 10718 10487 4676 31 10795 10562 4723 12 10844 10610 4770 30 10911 10677 4821 32 10980 10743 4856 30 11088 10850 4874 28 11157 10916 4919 78 11219 10975 4973 36 11290 11043 5030 83 11347 11098 5065 37 11410 11160 5116 32 11491 11240 5173 73 11569 11316 5219 102 11664 11411 5260 121 11740 11484 5290 20 11826 11565 5330 26 11890 11629 5368 32 11956 11688 5416 25 12046 11777 5452 46 12133 11862 5501 23 12216 11940 5560 52 12269 11989 5587 40 12365 12079 5645 45 12443 12154 5695 57 12515 12219 5747 18 12597 12299 5781 6 12702 12402 5808 23 12774 12468 5854 30 12855 12545 5897 6 12936 12625 5958 39 13023 12708 5999 92 13099 12779 6059 85 13204 12881 6108 104 13310 12981 6122 94 13383 13052 6171 27 13434 13102 6220 66 13486 13153 6270 50 13585 13248 6321 42 13696 13356 6352 17 13778 13437 6392 48 13850 13508 6424 57 13936 13584 6478 25 14008 13649 6524 14 14091 13724 6579 50 14162 13791 6618 61 14253 13875 6655 23 14326 13945 6696 9 14388 14002 6744 4 14443 14057 6795 13 14527 14141 6841 40 14606 14215 6876 59 14695 14299 6923 87 14760 14364 6968 11 14819 14423 7027 42 14834 14438 7027 42 14850 14454 7027 42 14882 14486 7027 42 14946 14550 7027 42 15072 14675 7027 42 15209 14812 7027 42 15350 14952 7027 42 15469 15070 7027 42 15580 15181 7027 42 15698 15297 7027 42 15847 15446 7027 42 15917 15507 7079 48 16036 15617 7110 67 16093 15673 7165 18 16154 15731 7220 21 16233 15806 7277 49 16336 15906 7308 70 16440 16009 7338 89 16555 16123 7369 108 16654 16215 7400 55 16734 16295 7442 22 16827 16386 7485 45 16909 16466 7543 90 16986 16540 7582 61 17040 16593 7632 12 17124 16673 7668 16 17214 16763 7700 28 17300 16846 7726 34 17385 16929 7767 51 17448 16992 7816 56 17506 17048 7870 46 17603 17142 7925 34 17711 17247 7965 22 17790 17325 8004 41 17878 17407 8060 50 17929 17458 8111 19 18009 17537 8163 59 18103 17628 8211 21 18172 17693 8254 44 18249 17766 8321 18 18320 17832 8368 39 18379 17889 8419 28 18448 17955 8478 43 18535 18040 8523 46 18585 18090 8573 43 18664 18169 8618 54 18750 18253 8648 54 18838 18341 8698 72 18935 18438 8741 70 19037 18537 8775 49 19126 18625 8814 55 19198 18692 8862 60 19247 18740 8909 54 19304 18794 8960 53 19395 18881 8999 45 19507 18990 9023 56 19603 19084 9041 52 19700 19177 9084 44 19774 19251 9144 43 19844 19320 9182 24 19893 19366 9221 45 19979 19451 9265 80 20057 19526 9317 58 20106 19575 9360 27 20164 19630 9412 34 20238 19698 9464 80 20332 19782 9502 38 20439 19888 9533 54 20535 19980 9561 12 20592 20036 9616 13 20644 20087 9666 36 20706 20146 9708 27 20815 20249 9727 41 20916 20348 9752 56 20985 20416 9769 44 21044 20471 9812 41 21111 20537 9845 38 21205 20627 9909 56 21304 20720 9938 74 21391 20799 9971 96 21490 20895 10010 117 21505 20910 10010 117 21521 20926 10010 117 21553 20957 10010 117 499 variables, 519 constraints, 1888 non-zeros
0 general integer, 463 binary, 36 continuous
BEST_INT_OBJ BEST_RELAX_OBJ REL_GAP(%) TIME
0 NA 6.8350736082e+000 NA 0.1
0 2.3702859021e+001 6.8350736082e+000 71.16 0.1
0 2.3702859021e+001 6.8350736082e+000 71.16 0.1
terminated. Time = 0.00
0 2.3702856799e+001 6.8352497613e+000 71.16 0.2
0 2.3702856799e+001 6.8352497613e+000 71.16 0.2
0 2.3702856799e+001 6.8352497613e+000 71.16 0.2
0 2.3702856799e+001 6.8352497613e+000 71.16 0.2
0 2.3702856799e+001 6.8352497613e+000 71.16 0.3
0 2.3702856799e+001 6.8352497613e+000 71.16 0.3
0 2.3702856799e+001 6.8352497613e+000 71.16 0.3
0 2.3702856799e+001 6.8352497613e+000 71.16 0.4
0 2.3702856799e+001 6.8352497613e+000 71.16 0.4
0 2.3702856799e+001 6.8352497613e+000 71.16 0.4
0 2.3702856799e+001 6.8352497613e+000 71.16 0.5
0 2.3702856799e+001 6.8352497613e+000 71.16 0.5
0 2.3702856799e+001 6.8352497613e+000 71.16 0.6
2.3702856799e+001 6.8430300728e+000 71.13 0.6
2.3702856799e+001 6.8528023197e+000 71.09 0.6
13 2.3702856799e+001 6.8528023197e+000 71.09 0.6
2.3702856799e+001 6.8528023197e+000 71.09 0.7
2.3702856799e+001 6.8536536940e+000 71.09 0.7
2.3702856799e+001 6.9533999840e+000 70.66 0.8
2.3702856799e+001 6.9773804508e+000 70.56 0.8
2.3702856799e+001 7.1401742815e+000 69.88 0.8
2.3702856799e+001 8.1463479070e+000 65.63 0.8
2.3702856799e+001 8.1497077167e+000 65.62 0.9
2.3702856799e+001 8.5253638462e+000 64.03 0.9
2.3702856799e+001 8.5753204925e+000 63.82 1.0
2.3702856799e+001 8.7021507888e+000 63.29 1.0
2.3702856799e+001 8.8210727185e+000 62.78 1.0
2.3702856799e+001 8.8210727185e+000 62.78 1.1
2.3702856799e+001 8.8670387507e+000 62.59 1.1
2.3702856799e+001 9.0604948152e+000 61.77 1.1
2.3702856799e+001 9.0762528179e+000 61.71 1.2
2.3702856799e+001 9.0762528179e+000 61.71 1.2
2.3702856799e+001 9.3363958227e+000 60.61 1.3
2.3702856799e+001 9.3721092019e+000 60.46 1.3
2.3702856799e+001 9.3897844742e+000 60.39 1.3
2.3702856799e+001 9.4897174629e+000 59.96 1.4
2.3702856799e+001 9.4897174629e+000 59.96 1.4
2.3694457704e+001 9.4897174629e+000 59.95 1.5
2.3694457704e+001 9.6220449093e+000 59.39 1.5
2.3691039369e+001 9.7476690850e+000 58.86 1.5
2.3691039369e+001 9.7549894486e+000 58.82 1.6
2.3691039369e+001 9.8390694073e+000 58.47 1.6
2.3691039369e+001 9.8559894886e+000 58.40 1.7
2.3691039369e+001 9.8795348834e+000 58.30 1.7
2.3691039369e+001 9.8795348834e+000 58.30 1.7
2.3691039369e+001 9.8895148238e+000 58.26 1.8
2.3691039369e+001 9.8895148238e+000 58.26 1.8
2.3691039369e+001 9.8925481181e+000 58.24 1.8
2.3691039369e+001 9.9258581444e+000 58.10 1.9
2.3691039369e+001 9.9358161235e+000 58.06 1.9
2.3691039369e+001 9.9358161235e+000 58.06 2.0
2.3691039369e+001 9.9710114781e+000 57.91 2.0
2.3691039369e+001 9.9811730477e+000 57.87 2.0
2.3691039369e+001 9.9978843171e+000 57.80 2.1
2.3691039369e+001 1.0027587275e+001 57.67 2.1
2.3691039369e+001 1.0057560998e+001 57.55 2.1
2.3691039369e+001 1.0079612714e+001 57.45 2.2
2.3691039369e+001 1.0093100677e+001 57.40 2.2
2.3691039369e+001 1.0093100677e+001 57.40 2.3
2.3691039369e+001 1.0100053871e+001 57.37 2.3
2.3691039369e+001 1.0107621572e+001 57.34 2.3
2.3691039369e+001 1.0115626347e+001 57.30 2.4
2.3691039369e+001 1.0128824152e+001 57.25 2.4
2.3691039369e+001 1.0128824152e+001 57.25 2.5
2.3691039369e+001 1.0136291850e+001 57.21 2.5
2.3691039369e+001 1.0136938820e+001 57.21 2.5
2.3691039369e+001 1.0157097950e+001 57.13 2.6
2.3691039369e+001 1.0167115345e+001 57.08 2.6
2.3691039369e+001 1.0176826138e+001 57.04 2.7
2.3691039369e+001 1.0181588979e+001 57.02 2.7
2.3691039369e+001 1.0182518612e+001 57.02 2.7
2.3691039369e+001 1.0193664358e+001 56.97 2.8
2.3691039369e+001 1.0204321301e+001 56.93 2.8
2.3691039369e+001 1.0206102439e+001 56.92 2.9
2.3691039369e+001 1.0213930705e+001 56.89 2.9
2.3691039369e+001 1.0215109342e+001 56.88 2.9
2.3691039369e+001 1.0222155376e+001 56.85 3.0
2.3691039369e+001 1.0232555119e+001 56.81 3.0
2.3691039369e+001 1.0234907256e+001 56.80 3.1
2.3691039369e+001 1.0243032961e+001 56.76 3.2
2.3691039369e+001 1.0248820049e+001 56.74 3.2
2.3691039369e+001 1.0260655104e+001 56.69 3.3
2.3691039369e+001 1.0260655104e+001 56.69 3.3
2.3691039369e+001 1.0262175763e+001 56.68 3.4
2.3691039369e+001 1.0270364603e+001 56.65 3.4
2.3691039369e+001 1.0272432487e+001 56.64 3.4
2.3691039369e+001 1.0311913446e+001 56.47 3.5
2.3691039369e+001 1.0313881020e+001 56.47 3.5
2.3691039369e+001 1.0317910680e+001 56.45 3.5
2.3691039369e+001 1.0327409679e+001 56.41 3.6
2.3691039369e+001 1.0337657094e+001 56.36 3.6
2.3691039369e+001 1.0347370702e+001 56.32 3.7
2.3691039369e+001 1.0349382429e+001 56.32 3.7
2.3691039369e+001 1.0349382429e+001 56.32 3.8
2.3691039369e+001 1.0360677561e+001 56.27 3.8
2.3691039369e+001 1.0372274906e+001 56.22 3.8
2.3691039369e+001 1.0382234745e+001 56.18 3.9
2.3691039369e+001 1.0398140848e+001 56.11 3.9
2.3691039369e+001 1.0398140848e+001 56.11 3.9
2.3691039369e+001 1.0404534417e+001 56.08 4.0
2.3691039369e+001 1.0404534417e+001 56.08 4.0
2.3691039369e+001 1.0404534417e+001 56.08 4.0
2.3691039369e+001 1.0404534417e+001 56.08 4.0
2.3691039369e+001 1.0404534417e+001 56.08 4.0
2.3691039369e+001 1.0404534417e+001 56.08 4.1
2.3691039369e+001 1.0404534417e+001 56.08 4.1
2.3691039369e+001 1.0404534417e+001 56.08 4.1
2.3691039369e+001 1.0404534417e+001 56.08 4.2
2.3676148312e+001 1.0404534417e+001 56.05 4.2
2.3676148312e+001 1.0404534417e+001 56.05 4.2
2.3676148312e+001 1.0404534417e+001 56.05 4.3
2.3676148312e+001 1.0404534417e+001 56.05 4.3
2.3676148312e+001 1.0413468335e+001 56.02 4.3
2.3676148312e+001 1.0429549649e+001 55.95 4.4
2.3676148312e+001 1.0433156080e+001 55.93 4.4
2.3676148312e+001 1.0442743364e+001 55.89 4.5
2.3676148312e+001 1.0442743364e+001 55.89 4.5
2.3676148312e+001 1.0442743364e+001 55.89 4.5
2.3676148312e+001 1.0447425261e+001 55.87 4.6
2.3676148312e+001 1.0470489467e+001 55.78 4.6
2.3676148312e+001 1.0487647023e+001 55.70 4.7
2.3676148312e+001 1.0487650426e+001 55.70 4.7
2.3676148312e+001 1.0487650426e+001 55.70 4.7
2.3676148312e+001 1.0504264133e+001 55.63 4.8
2.3676148312e+001 1.0521138635e+001 55.56 4.8
2.3676148312e+001 1.0548741635e+001 55.45 4.8
2.3676148312e+001 1.0560592576e+001 55.40 4.9
2.3676148312e+001 1.0580854106e+001 55.31 4.9
2.3676148312e+001 1.0580854106e+001 55.31 5.0
2.3676148312e+001 1.0580854106e+001 55.31 5.0
2.3676148312e+001 1.0601887718e+001 55.22 5.0
2.3676148312e+001 1.0609328020e+001 55.19 5.1
2.3676148312e+001 1.0628734367e+001 55.11 5.1
2.3676148312e+001 1.0648407398e+001 55.02 5.1
2.3676148312e+001 1.0674856277e+001 54.91 5.2
2.3676148312e+001 1.0689274546e+001 54.85 5.2
2.3676148312e+001 1.0690805077e+001 54.85 5.2
2.3676148312e+001 1.0720452713e+001 54.72 5.3
2.3676148312e+001 1.0725814494e+001 54.70 5.3
2.3676148312e+001 1.0729297994e+001 54.68 5.3
2.3676148312e+001 1.0744962962e+001 54.62 5.4
2.3676148312e+001 1.0760622349e+001 54.55 5.4
2.3676148312e+001 1.0761414323e+001 54.55 5.5
2.3676148312e+001 1.0761414323e+001 54.55 5.5
2.3676148312e+001 1.0761414323e+001 54.55 5.5
2.3676148312e+001 1.0826803617e+001 54.27 5.6
2.3676148312e+001 1.0835165501e+001 54.24 5.6
2.3676148312e+001 1.0852636929e+001 54.16 5.6
2.3676148312e+001 1.0858041825e+001 54.14 5.7
2.3676148312e+001 1.0864050171e+001 54.11 5.7
2.3676148312e+001 1.0895185792e+001 53.98 5.8
2.3676148312e+001 1.0902282796e+001 53.95 5.8
2.3676148312e+001 1.0905972326e+001 53.94 5.8
2.3676148312e+001 1.0914049902e+001 53.90 5.9
2.3676148312e+001 1.0930119600e+001 53.83 5.9
2.3676148312e+001 1.0964200233e+001 53.69 5.9
2.3676148312e+001 1.0971196677e+001 53.66 6.0
2.3676148312e+001 1.0984523023e+001 53.61 6.0
2.3676148312e+001 1.0999014845e+001 53.54 6.0
2.3676148312e+001 1.1011275561e+001 53.49 6.1
2.3676148312e+001 1.1019539156e+001 53.46 6.1
2.3676148312e+001 1.1026096665e+001 53.43 6.1
2.3676148312e+001 1.1049685269e+001 53.33 6.2
2.3676148312e+001 1.1051398696e+001 53.32 6.2
2.3676148312e+001 1.1060025651e+001 53.29 6.3
2.3676148312e+001 1.1066018005e+001 53.26 6.3
2.3676148312e+001 1.1066018005e+001 53.26 6.3
2.3676148312e+001 1.1070968268e+001 53.24 6.4
2.3676148312e+001 1.1073050544e+001 53.23 6.4
2.3676148312e+001 1.1087502370e+001 53.17 6.4
2.3676148312e+001 1.1096893686e+001 53.13 6.5
2.3676148312e+001 1.1106291667e+001 53.09 6.5
2.3676148312e+001 1.1118730763e+001 53.04 6.5
2.3676148312e+001 1.1123336314e+001 53.02 6.6
2.3676148312e+001 1.1130316278e+001 52.99 6.6
2.3676148312e+001 1.1144749615e+001 52.93 6.7
2.3676148312e+001 1.1148315741e+001 52.91 6.7
2.3676148312e+001 1.1175119744e+001 52.80 6.7
2.3676148312e+001 1.1177151687e+001 52.79 6.8
2.3676148312e+001 1.1202270323e+001 52.69 6.8
2.3676148312e+001 1.1202270323e+001 52.69 6.8
2.3676148312e+001 1.1202270323e+001 52.69 6.9
2.3676148312e+001 1.1227144924e+001 52.58 6.9
2.3676148312e+001 1.1242468542e+001 52.52 7.0
2.3676148312e+001 1.1248445006e+001 52.49 7.0
2.3676148312e+001 1.1264923157e+001 52.42 7.0
2.3676148312e+001 1.1265502127e+001 52.42 7.1
2.3676148312e+001 1.1275159245e+001 52.38 7.1
2.3676148312e+001 1.1299194792e+001 52.28 7.2
2.3676148312e+001 1.1299194792e+001 52.28 7.2
2.3676148312e+001 1.1307439084e+001 52.24 7.2
2.3676148312e+001 1.1339024354e+001 52.11 7.3
2.3676148312e+001 1.1343980975e+001 52.09 7.3
2.3676148312e+001 1.1345738931e+001 52.08 7.3
2.3676148312e+001 1.1354081084e+001 52.04 7.4
2.3676148312e+001 1.1376781948e+001 51.95 7.4
2.3676148312e+001 1.1380821702e+001 51.93 7.5
2.3676148312e+001 1.1386572251e+001 51.91 7.5
2.3676148312e+001 1.1390213180e+001 51.89 7.5
2.3676148312e+001 1.1402517682e+001 51.84 7.6
2.3676148312e+001 1.1407881718e+001 51.82 7.6
2.3676148312e+001 1.1416578898e+001 51.78 7.6
2.3676148312e+001 1.1416578898e+001 51.78 7.7
2.3676148312e+001 1.1416578898e+001 51.78 7.7
2.3676148312e+001 1.1416578898e+001 51.78 7.7
2.3676148312e+001 1.1416578898e+001 51.78 7.7
2.3676148312e+001 1.1416578898e+001 51.78 7.7
2.3676148312e+001 1.1416578898e+001 51.78 7.8
2.3676148312e+001 1.1416578898e+001 51.78 7.8
2.3676148312e+001 1.1416578898e+001 51.78 7.8
2.3676148312e+001 1.1416578898e+001 51.78 7.9
2.3676148312e+001 1.1416578898e+001 51.78 7.9
2.3676148312e+001 1.1416578898e+001 51.78 7.9
2.3676148312e+001 1.1424182162e+001 51.75 8.0
2.3676148312e+001 1.1424182162e+001 51.75 8.0
2.3676148312e+001 1.1427441657e+001 51.73 8.0
2.3676148312e+001 1.1444486365e+001 51.66 8.1
2.3676148312e+001 1.1460526309e+001 51.59 8.1
2.3676148312e+001 1.1460526309e+001 51.59 8.1
2.3676148312e+001 1.1472512136e+001 51.54 8.2
2.3676148312e+001 1.1477772587e+001 51.52 8.2
2.3676148312e+001 1.1477772587e+001 51.52 8.3
2.3676148312e+001 1.1485859031e+001 51.49 8.3
2.3676148312e+001 1.1509351307e+001 51.39 8.3
2.3676148312e+001 1.1524826800e+001 51.32 8.3
2.3676148312e+001 1.1527104971e+001 51.31 8.4
2.3676148312e+001 1.1568301248e+001 51.14 8.4
2.3676148312e+001 1.1574032118e+001 51.12 8.5
2.3676148312e+001 1.1575838003e+001 51.11 8.5
2.3676148312e+001 1.1585360258e+001 51.07 8.5
2.3676148312e+001 1.1587452954e+001 51.06 8.6
2.3676148312e+001 1.1601962488e+001 51.00 8.6
2.3676148312e+001 1.1609676052e+001 50.96 8.7
2.3676148312e+001 1.1621222397e+001 50.92 8.7
2.3676148312e+001 1.1625272412e+001 50.90 8.7
2.3676148312e+001 1.1641788914e+001 50.83 8.8
2.3676148312e+001 1.1651655235e+001 50.79 8.8
2.3676148312e+001 1.1659164281e+001 50.76 8.8
2.3676148312e+001 1.1660654355e+001 50.75 8.9
2.3676148312e+001 1.1660654355e+001 50.75 8.9
2.3676148312e+001 1.1672936686e+001 50.70 9.0
2.3676148312e+001 1.1681991608e+001 50.66 9.0
2.3676148312e+001 1.1687151183e+001 50.64 9.0
2.3676148312e+001 1.1696724622e+001 50.60 9.1
2.3676148312e+001 1.1707152872e+001 50.55 9.1
2.3676148312e+001 1.1711144057e+001 50.54 9.2
2.3676148312e+001 1.1720229618e+001 50.50 9.2
2.3676148312e+001 1.1727269004e+001 50.47 9.2
2.3676148312e+001 1.1729693628e+001 50.46 9.3
2.3676148312e+001 1.1738411491e+001 50.42 9.3
2.3676148312e+001 1.1739255730e+001 50.42 9.3
2.3676148312e+001 1.1739255730e+001 50.42 9.4
2.3676148312e+001 1.1758226529e+001 50.34 9.4
2.3676148312e+001 1.1764129505e+001 50.31 9.4
2.3676148312e+001 1.1775324330e+001 50.27 9.5
2.3676148312e+001 1.1779039101e+001 50.25 9.5
2.3676148312e+001 1.1782026849e+001 50.24 9.5
2.3676148312e+001 1.1782026849e+001 50.24 9.6
2.3676148312e+001 1.1782026849e+001 50.24 9.6
2.3676148312e+001 1.1782026849e+001 50.24 9.7
2.3676148312e+001 1.1804608785e+001 50.14 9.7
2.3676148312e+001 1.1809910205e+001 50.12 9.7
2.3676148312e+001 1.1812302261e+001 50.11 9.8
2.3676148312e+001 1.1814789309e+001 50.10 9.8
2.3676148312e+001 1.1817999595e+001 50.08 9.8
2.3676148312e+001 1.1822231574e+001 50.07 9.9
2.3676148312e+001 1.1828571116e+001 50.04 9.9
2.3662438951e+001 1.1830727737e+001 50.00 9.9
2.3662438951e+001 1.1831995390e+001 50.00 10.0
2.3662438951e+001 1.1831995390e+001 50.00 10.0
2.3662438951e+001 1.1838791534e+001 49.97 10.0
2.3662438951e+001 1.1842663348e+001 49.95 10.1
2.3662438951e+001 1.1846647490e+001 49.93 10.1
2.3662438951e+001 1.1847940219e+001 49.93 10.2
2.3662438951e+001 1.1847940219e+001 49.93 10.2
2.3662438951e+001 1.1847940219e+001 49.93 10.2
2.3662438951e+001 1.1847940219e+001 49.93 10.3
2.3662438951e+001 1.1857269322e+001 49.89 10.3
2.3662438951e+001 1.1860558374e+001 49.88 10.3
2.3662438951e+001 1.1865358816e+001 49.86 10.4
2.3662438951e+001 1.1865358816e+001 49.86 10.4
2.3662438951e+001 1.1865358816e+001 49.86 10.4
2.3662438951e+001 1.1865358816e+001 49.86 10.5
2.3662438951e+001 1.1865358816e+001 49.86 10.5
2.3662438951e+001 1.1865358816e+001 49.86 10.5
2.3662438951e+001 1.1865358816e+001 49.86 10.5

I have not save the last iteration yet, so I need to rerun the program. I remember the rea gap in the last iterations reduces more and more slower as the time goes. And I didn’t use bigM. just linear constraints with too many binary variables.

TomK · July 19, 2020, 7:36am

It runs for some time now, the gap just decay slowly.

440173 424472 254942 52 2.3567895329e+001 1.3639235103e+001 42.13 203.9
440252 424550 254987 64 2.3567895329e+001 1.3639280194e+001 42.13 204.0
440322 424619 255025 42 2.3567895329e+001 1.3639300862e+001 42.13 204.0
440379 424676 255072 25 2.3567895329e+001 1.3639383233e+001 42.13 204.0
440458 424755 255103 64 2.3567895329e+001 1.3639392780e+001 42.13 204.1
440545 424839 255146 77 2.3567895329e+001 1.3639407920e+001 42.13 204.1
440619 424912 255206 53 2.3567895329e+001 1.3639463733e+001 42.13 204.2
440704 424994 255247 56 2.3567895329e+001 1.3639468451e+001 42.13 204.2
440781 425070 255296 47 2.3567895329e+001 1.3639509331e+001 42.13 204.3
440850 425132 255345 65 2.3567895329e+001 1.3639544407e+001 42.13 204.3
440928 425208 255383 67 2.3567895329e+001 1.3639552333e+001 42.13 204.3
441003 425281 255432 26 2.3567895329e+001 1.3639579198e+001 42.13 204.4
441068 425345 255479 38 2.3567895329e+001 1.3639633495e+001 42.13 204.4
441188 425456 255515 55 2.3567895329e+001 1.3639672869e+001 42.13 204.4
441270 425537 255555 46 2.3567895329e+001 1.3639705716e+001 42.13 204.5
441344 425611 255617 40 2.3567895329e+001 1.3639775538e+001 42.13 204.5
441413 425679 255650 54 2.3567895329e+001 1.3639775538e+001 42.13 204.6
441513 425777 255690 42 2.3567895329e+001 1.3639789675e+001 42.13 204.6
441571 425834 255738 44 2.3567895329e+001 1.3639801055e+001 42.13 204.7
441658 425920 255775 28 2.3567895329e+001 1.3639848889e+001 42.13 204.7
441727 425989 255816 45 2.3567895329e+001 1.3639878651e+001 42.13 204.7
441808 426070 255867 58 2.3567895329e+001 1.3639963076e+001 42.12 204.8
441906 426163 255897 56 2.3567895329e+001 1.3640014778e+001 42.12 204.8
441975 426232 255950 50 2.3567895329e+001 1.3640073638e+001 42.12 204.9
442060 426311 255983 31 2.3567895329e+001 1.3640110143e+001 42.12 204.9
442138 426385 256017 18 2.3567895329e+001 1.3640142145e+001 42.12 204.9
442214 426457 256071 45 2.3567895329e+001 1.3640213686e+001 42.12 205.0
442312 426548 256113 58 2.3567895329e+001 1.3640261601e+001 42.12 205.0
442379 426612 256168 64 2.3567895329e+001 1.3640272817e+001 42.12 205.1
442460 426690 256205 48 2.3567895329e+001 1.3640331808e+001 42.12 205.1
442537 426767 256252 47 2.3567895329e+001 1.3640339295e+001 42.12 205.1
442615 426842 256298 21 2.3567895329e+001 1.3640364465e+001 42.12 205.2
442693 426917 256354 41 2.3567895329e+001 1.3640448036e+001 42.12 205.2
442787 427004 256402 43 2.3567895329e+001 1.3640448036e+001 42.12 205.3
442866 427082 256455 52 2.3567895329e+001 1.3640523608e+001 42.12 205.3
442927 427142 256498 46 2.3567895329e+001 1.3640618342e+001 42.12 205.3
443013 427225 256552 61 2.3567895329e+001 1.3640618342e+001 42.12 205.4
443107 427317 256602 35 2.3567895329e+001 1.3640651523e+001 42.12 205.4
443200 427405 256641 70 2.3567895329e+001 1.3640711700e+001 42.12 205.5
443265 427468 256698 78 2.3567895329e+001 1.3640747499e+001 42.12 205.5
443337 427534 256746 34 2.3567895329e+001 1.3640787835e+001 42.12 205.5
443410 427605 256791 50 2.3567895329e+001 1.3640823126e+001 42.12 205.6
443472 427665 256843 37 2.3567895329e+001 1.3640850076e+001 42.12 205.6

Michal_Adamaszek · July 19, 2020, 7:42am

It doesn’t look like a issue with CVX so perhaps this is not the right forum. The number of active nodes is growing so you probably are running out of RAM indeed. Suggestions:

Try Mosek 9.2
Save the problem to a task file (instructions in https://docs.mosek.com/9.2/faq/faq.html#how-do-i-dump-the-problem-to-a-file-to-attach-with-my-support-question) and send to support@mosek.com. Please also write a few words about the mathematical model/type of problem you are trying to solve.

TomK · July 19, 2020, 7:53am

Thanks for your kind suggestions. and I dont konw why I can’t install the bundled version CVX 2.2(only the mosek9 (it says cant find module?), however, the sdpt3 and sedumi can be installed well). and I want to ask that Who is faster in MIDP between Gurobi and Mosek.

Mark_L_Stone · July 19, 2020, 10:17am

Install Mosek under MATLAB, and use mosekfiag to make sure it is installed correctly. Then re-run cvx_setup. Then iisue the command cvx_solver. You may see more than one version of Mosek listed. Choose the specific version corresponding to Mosek 9.2.x., such as Mosek_9, i.e. cvx_solver mosek_9. After that, you can continue to update to the latest version of Mosek 9.2.x without re-running cvx_setup.

TomK · July 19, 2020, 12:35pm

You mean I need to install Mosek separately?

Mark_L_Stone · July 19, 2020, 2:20pm

As of now, if you want Mosek 9.2, yes.

Based on your using Mosek 8.0.0.60, I am guessing you are using CVX 2.1. Please install CVX 2.2 http://cvxr.com/cvx/download/; that comes with Mosek 9.1.something. Separately install Mosek to get the latest version of Mosek, currently 9.2.14.

Michal_Adamaszek · July 19, 2020, 6:01pm

If you can use Mosek 9.whatever from cvx that’s OK. There will not be a huge difference between the various 9.X.