Hi Astghik,

you can try to set the focus http://www.gurobi.com/documentation/8.1/refman/mipfocus.html#parameter:MIPFocus or the heuristics http://www.gurobi.com/documentation/8.1/refman/heuristics.html#parameter:Heuristics

Can you post an except of the logging?

Hi there,

I am facing the same issue in solving a MIQP optimisation problem. To be specific I am following Model Predictive Control technique so at each step in the algorithm I need to perform a MIQP with Gurobi. However, I am struggling to solve the optimisation problem at the very first step.

The log file is as below.

Academic license - for non-commercial use onlyOptimize a model with 7010 rows, 6010 columns and 16910 nonzerosModel has 1010 quadratic objective termsVariable types: 2010 continuous, 4000 integer (4000 binary)Coefficient statistics:Matrix range [4e-02, 2e+00]Objective range [5e+01, 4e+02]QObjective range [2e+00, 2e+00]Bounds range [1e+00, 1e+00]RHS range [9e-02, 2e+01]Found heuristic solution: objective 104373.43751Presolve removed 4100 rows and 1100 columnsPresolve time: 0.02sPresolved: 2910 rows, 4910 columns, 10910 nonzerosPresolved model has 1010 quadratic objective termsVariable types: 1010 continuous, 3900 integer (3000 binary)Root relaxation: objective 2.410519e-01, 7085 iterations, 0.13 secondsNodes | Current Node | Objective Bounds | WorkExpl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time0 0 0.24105 0 1250 104373.438 0.24105 100% - 0sH 0 0 1421.0937571 0.24105 100% - 0sH 0 0 970.3784353 0.24105 100% - 0s0 0 0.24105 0 1250 970.37844 0.24105 100% - 0sH 0 0 13.3088811 0.24105 98.2% - 1sH 0 0 13.1119684 0.24105 98.2% - 2s0 2 0.24105 0 1250 13.11197 0.24105 98.2% - 2sH 1 4 2.5391907 0.24105 90.5% 0.0 2sH 2 4 2.4837433 0.24105 90.3% 1.5 2sH 168 169 2.4770202 0.24105 90.3% 2.6 3sH 200 200 2.4751562 0.24105 90.3% 2.6 4sH 202 202 2.4709689 0.24105 90.2% 2.6 4sH 204 204 2.4337335 0.24105 90.1% 2.6 4sH 206 206 2.3969387 0.24105 89.9% 2.6 4sH 208 208 2.2607556 0.24105 89.3% 2.6 4sH 232 233 1.5884368 0.24105 84.8% 2.7 5sH 233 234 1.1954716 0.24105 79.8% 2.7 5sH 239 240 1.1782665 0.24105 79.5% 2.7 5sH 240 241 1.1327982 0.24105 78.7% 2.7 5sH 242 243 1.0993180 0.24105 78.1% 2.7 5sH 274 275 1.0984273 0.24105 78.1% 2.7 6sH 276 277 1.0907321 0.24105 77.9% 2.7 6sH 300 299 0.8630261 0.24105 72.1% 2.7 7sH 316 317 0.8630261 0.24105 72.1% 2.7 7sH 318 319 0.8629522 0.24105 72.1% 2.7 7sH 520 521 0.8604217 0.24105 72.0% 2.9 8sH 524 525 0.8573929 0.24105 71.9% 2.9 8sH 591 592 0.8573322 0.24105 71.9% 3.0 9s592 593 0.24968 101 1212 0.85733 0.24105 71.9% 3.0 10sH 600 593 0.8023654 0.24105 70.0% 3.0 10sH 609 602 0.7538245 0.24105 68.0% 3.0 10sH 1132 1118 0.7516816 0.24105 67.9% 3.8 11sH 1204 1190 0.7512557 0.24105 67.9% 4.0 11sH 1400 1386 0.7443362 0.24105 67.6% 4.0 12sH 1448 1434 0.6360757 0.24105 62.1% 3.9 12sH 1520 1502 0.6015006 0.24105 59.9% 3.9 13sH 1560 1542 0.5978622 0.24105 59.7% 3.8 13s1878 1878 0.31499 312 988 0.59786 0.24105 59.7% 3.8 15sH 1946 1925 0.5927228 0.24105 59.3% 3.9 15sH 1992 1971 0.5729533 0.24105 57.9% 3.9 15sH 2958 2935 0.5566761 0.24105 56.7% 4.0 15sH 3089 3090 0.5496940 0.24105 56.1% 4.0 17sH 3100 3092 0.5477793 0.24105 56.0% 4.0 18sH 3111 3103 0.5477793 0.24105 56.0% 4.0 18s3156 3157 0.32337 533 667 0.54778 0.24105 56.0% 4.0 20sH 3200 3157 0.5456818 0.24105 55.8% 4.0 20sH 3245 3202 0.5354913 0.24105 55.0% 4.0 20sH 3335 3293 0.5351017 0.24105 55.0% 4.0 20sH 3925 3902 0.4911755 0.24105 50.9% 4.0 23s4026 4027 0.32529 677 464 0.49118 0.24105 50.9% 4.0 25sH 4100 4027 0.4752007 0.24105 49.3% 4.1 25sH 4525 4502 0.4713088 0.24105 48.9% 4.1 27sH 4700 4628 0.4690689 0.24105 48.6% 4.1 28sH 5075 5002 0.4674786 0.24105 48.4% 4.2 28s5076 5077 0.38353 849 354 0.46748 0.24105 48.4% 4.2 30sH 5175 5150 0.4450028 0.24105 45.8% 4.2 30sH 5450 5375 0.4421985 0.24105 45.5% 4.1 32sH 5750 5727 0.4416897 0.24105 45.4% 4.2 33sH 5826 5824 0.4411571 0.24105 45.4% 4.2 34s5832 5829 0.38441 964 248 0.44116 0.24105 45.4% 4.2 36sH 5900 5829 0.4400315 0.24105 45.2% 4.2 36sH 6107 6037 0.4395533 0.24105 45.2% 4.3 36sH 6176 6105 0.4311664 0.24105 44.1% 4.3 36sH 6246 6243 0.4297099 0.24105 43.9% 4.3 37sH 6250 5933 0.4291423 0.24105 43.8% 4.3 39sH 6252 5640 0.4248724 0.24105 43.3% 5.4 39sH 6253 5358 0.4221323 0.24105 42.9% 5.4 39s6278 5386 0.24135 17 1249 0.42213 0.24105 42.9% 5.4 40sH 6340 5149 0.4218470 0.24105 42.9% 5.4 41sH 6604 5068 0.4218470 0.24105 42.9% 5.3 41sH 6684 4877 0.4204196 0.24105 42.7% 5.2 43sH 6694 4658 0.4200950 0.24105 42.6% 5.2 43sH 6890 4570 0.4169799 0.24105 42.2% 5.2 43s7012 4647 0.24782 43 1216 0.41698 0.24105 42.2% 5.2 46sH 7054 4465 0.4148401 0.24105 41.9% 5.2 47sH 7056 4269 0.4145561 0.24105 41.9% 5.2 47sH 7067 4090 0.4130168 0.24105 41.6% 5.2 48sH 7069 3913 0.4117717 0.24105 41.5% 5.2 48sH 7070 3745 0.4096722 0.24105 41.2% 5.2 48s7261 3859 0.25110 53 1197 0.40967 0.24105 41.2% 5.1 51sH 7268 3707 0.4090244 0.24105 41.1% 5.1 51sH 7488 3684 0.4075136 0.24105 40.8% 5.1 52sH 7502 3545 0.4065043 0.24105 40.7% 5.1 54s7623 3613 0.25701 65 1161 0.40650 0.24105 40.7% 5.1 55sH 7931 3653 0.4046611 0.24105 40.4% 5.0 56sH 8069 3608 0.4031296 0.24105 40.2% 5.0 57sH 8270 3606 0.4023847 0.24105 40.1% 5.1 58s8527 3760 0.25911 91 1143 0.40238 0.24105 40.1% 5.3 60sH 8533 3648 0.4013390 0.24105 39.9% 5.3 60sH 8685 3638 0.4009273 0.24105 39.9% 5.3 61sH 8732 3539 0.3996373 0.24105 39.7% 5.3 63s8789 3598 0.25945 99 1142 0.39964 0.24105 39.7% 5.3 65sH 8860 3534 0.3984287 0.24105 39.5% 5.3 65sH 9025 3539 0.3965691 0.24105 39.2% 5.3 66s9590 3863 0.29649 121 1144 0.39657 0.24105 39.2% 5.3 71sH 9600 3768 0.3952559 0.24105 39.0% 5.4 71s10769 4508 0.27239 148 1145 0.39526 0.24105 39.0% 5.4 75sH10855 4448 0.3929197 0.24105 38.7% 5.4 75sH11967 5448 0.3929197 0.24105 38.7% 5.4 77sH12001 5482 0.3927632 0.24105 38.6% 5.4 77sH12069 5550 0.3927275 0.24105 38.6% 5.4 77s12138 5648 0.29799 181 1142 0.39273 0.24105 38.6% 5.4 80sH12347 5797 0.3917445 0.24105 38.5% 5.4 80s12933 6444 0.33049 203 1128 0.39174 0.24105 38.5% 5.4 85sH13508 6954 0.3914108 0.24105 38.4% 5.4 85sH13878 7309 0.3912308 0.24105 38.4% 5.3 88sH14062 7492 0.3890542 0.24105 38.0% 5.4 88s14523 8023 0.33230 251 1038 0.38905 0.24105 38.0% 5.3 90sH15551 8975 0.3890542 0.24105 38.0% 5.3 93sH15755 9172 0.3886318 0.24105 38.0% 5.3 93sH15857 9274 0.3886318 0.24105 38.0% 5.3 93s16062 9487 0.33380 316 895 0.38863 0.24105 38.0% 5.3 96sH16334 9705 0.3881272 0.24105 37.9% 5.3 96sH16600 9840 0.3849535 0.24105 37.4% 5.3 99sH17481 10586 0.3843688 0.24105 37.3% 5.2 99s17482 10654 0.33505 371 799 0.38437 0.24105 37.3% 5.2 101sH17576 10725 0.3840332 0.24105 37.2% 5.3 101sH17709 10861 0.3830885 0.24105 37.1% 5.3 101s17710 10885 0.33696 380 810 0.38309 0.24105 37.1% 5.3 105sH18901 11844 0.3809896 0.24105 36.7% 5.3 105sH19000 11855 0.3777322 0.24105 36.2% 5.3 108sH19263 12111 0.3757423 0.24105 35.8% 5.4 108s20021 12948 0.35221 475 637 0.37574 0.24105 35.8% 5.4 112sH20500 13326 0.3753090 0.24105 35.8% 5.4 112sH20700 13516 0.3742276 0.24105 35.6% 5.4 112s20861 13765 0.35301 515 567 0.37423 0.24105 35.6% 5.4 115sH20940 13805 0.3742276 0.24105 35.6% 5.4 115sH21520 14216 0.3719106 0.24105 35.2% 5.5 118sH22161 14901 0.3717775 0.24105 35.2% 5.5 122sH22201 14936 0.3708983 0.24105 35.0% 5.5 122sH22241 14969 0.3705738 0.24105 35.0% 5.5 122s22641 15337 0.35423 585 417 0.37057 0.24105 35.0% 5.5 125sH22700 15238 0.3687503 0.24105 34.6% 5.5 125sH23540 15934 0.3677783 0.24105 34.5% 5.5 128s24041 16400 0.35536 635 307 0.36778 0.24105 34.5% 5.5 132sH24700 16941 0.3677436 0.24105 34.5% 5.6 132s24761 17041 0.35588 665 280 0.36774 0.24105 34.5% 5.6 135sH25080 17246 0.3672407 0.24105 34.4% 5.6 135sH25360 17399 0.3666663 0.24105 34.3% 5.6 139sH25880 17758 0.3663976 0.24105 34.2% 5.6 139s26061 17974 0.35604 715 240 0.36640 0.24105 34.2% 5.6 142sH26100 17917 0.3651768 0.24105 34.0% 5.6 142s26641 18377 0.35900 735 235 0.36518 0.24105 34.0% 5.7 145sH27336 18865 0.3651171 0.24105 34.0% 5.7 145sH27600 18596 0.3625397 0.24105 33.5% 5.7 149s28470 19453 0.35929 802 193 0.36254 0.24105 33.5% 5.7 152s29180 20128 0.35918 818 197 0.36254 0.24105 33.5% 5.7 156sH29601 20409 0.3618351 0.24105 33.4% 5.7 156sH29720 20506 0.3616458 0.24105 33.3% 5.7 156s30095 20917 0.36006 838 186 0.36165 0.24105 33.3% 5.7 161sH30123 20916 0.3612371 0.24105 33.3% 5.7 161sH30601 21181 0.3599682 0.24105 33.0% 5.7 161s31020 21577 cutoff 849 0.35997 0.24105 33.0% 5.7 166sH31100 21408 0.3588971 0.24105 32.8% 5.7 166s32686 23088 0.24854 98 1214 0.35890 0.24105 32.8% 5.7 172sH32800 23028 0.3584732 0.24105 32.8% 5.7 172sH32815 22986 0.3577178 0.24105 32.6% 5.7 172s33948 23972 0.25151 213 1129 0.35772 0.24105 32.6% 5.7 178sH34274 24201 0.3575482 0.24105 32.6% 5.7 178s35623 25529 0.26375 366 958 0.35755 0.24105 32.6% 5.6 183sH35720 25625 0.3575276 0.24105 32.6% 5.6 183sH35721 25540 0.3570697 0.24105 32.5% 5.6 183sH36721 26147 0.3557409 0.24105 32.2% 5.6 184s36977 26314 0.26524 462 794 0.35574 0.24105 32.2% 5.6 189sH37124 26337 0.3551684 0.24105 32.1% 5.6 189sH37720 26732 0.3547534 0.24105 32.1% 5.6 189s38369 27445 0.26878 586 669 0.35475 0.24105 32.1% 5.6 194sH38532 27569 0.3546466 0.24105 32.0% 5.6 194sH38896 27696 0.3538100 0.24105 31.9% 5.6 194s39892 28732 0.27040 718 490 0.35381 0.24105 31.9% 5.6 199sH39900 28709 0.3536087 0.24105 31.8% 5.6 199sH40946 29544 0.3532519 0.24105 31.8% 5.6 199s41183 29891 0.27144 827 410 0.35325 0.24105 31.8% 5.6 204sH41418 29886 0.3523701 0.24105 31.6% 5.6 204sH42495 30869 0.3514137 0.24105 31.4% 5.6 205sH42595 30793 0.3508061 0.24105 31.3% 5.6 205s42696 31008 0.27464 945 280 0.35081 0.24105 31.3% 5.6 210sH42905 31104 0.3506635 0.24105 31.3% 5.6 210sH42910 31177 0.3501381 0.24105 31.2% 5.6 210s44147 32389 0.28238 1050 152 0.35014 0.24105 31.2% 5.6 216sH45391 33535 0.3499254 0.24105 31.1% 5.6 216s45954 34147 0.32230 1201 34 0.34993 0.24105 31.1% 5.6 221sH47088 35180 0.3495196 0.24105 31.0% 5.6 221s47591 35681 0.25702 70 1154 0.34952 0.24105 31.0% 5.6 227sH48413 36394 0.3486554 0.24105 30.9% 5.6 227s48954 36953 0.27283 125 1133 0.34866 0.24105 30.9% 5.6 232sH49167 37025 0.3486554 0.24105 30.9% 5.6 232sH49436 37404 0.3486554 0.24105 30.9% 5.6 232sH49596 37526 0.3485910 0.24105 30.8% 5.6 232sH49685 37606 0.3484318 0.24105 30.8% 5.6 232s50392 38338 0.31864 160 1130 0.34843 0.24105 30.8% 5.6 238sH51590 39039 0.3457246 0.24105 30.3% 5.6 238s51700 39225 0.29785 215 1091 0.34572 0.24105 30.3% 5.6 244sH52202 39505 0.3455679 0.24105 30.2% 5.6 244sH53010 40378 0.3454030 0.24105 30.2% 5.6 244s53112 40572 0.29903 265 967 0.34540 0.24105 30.2% 5.6 250sH53767 40961 0.3439099 0.24105 29.9% 5.6 250s55461 42511 0.30150 360 771 0.34391 0.24105 29.9% 5.6 256sH56530 43343 0.3435564 0.24105 29.8% 5.6 256s57191 44103 0.31248 430 619 0.34356 0.24105 29.8% 5.6 261sH57630 44374 0.3431843 0.24105 29.8% 5.6 262sH58511 45226 0.3423715 0.24105 29.6% 5.6 267sH58618 45253 0.3419763 0.24105 29.5% 5.6 267s60482 47083 0.31533 538 347 0.34198 0.24105 29.5% 5.6 272sH61430 47695 0.3411162 0.24105 29.3% 5.6 272s61888 48139 0.31586 598 371 0.34112 0.24105 29.3% 5.6 278s63206 49370 0.31639 654 319 0.34112 0.24105 29.3% 5.6 283sH65279 51048 0.3404322 0.24105 29.2% 5.6 284s65566 51405 0.31701 752 229 0.34043 0.24105 29.2% 5.6 289s67154 52985 0.32189 819 191 0.34043 0.24105 29.2% 5.7 294sH67240 52951 0.3399960 0.24105 29.1% 5.7 294s68699 54226 0.32176 859 184 0.34000 0.24105 29.1% 5.7 299sH69196 54597 0.3398602 0.24105 29.1% 5.7 299sH69846 55220 0.3398602 0.24105 29.1% 5.7 304sH69900 55220 0.3398602 0.24105 29.1% 5.7 304sH70008 55171 0.3396575 0.24105 29.0% 5.7 304sH70058 55025 0.3392336 0.24105 28.9% 5.7 304s70819 55812 0.26583 722 491 0.33923 0.24105 28.9% 5.7 305s71217 56251 0.24405 38 1229 0.33923 0.24105 28.9% 5.7 310sH71384 55772 0.3380371 0.24105 28.7% 5.7 310s73262 57555 0.25623 80 1149 0.33804 0.24105 28.7% 5.7 316sH73592 57444 0.3374838 0.24105 28.6% 5.7 316s74802 58753 0.26076 108 1129 0.33748 0.24105 28.6% 5.7 321sH75392 58811 0.3366477 0.24105 28.4% 5.7 321sH75674 58647 0.3359925 0.24105 28.3% 5.7 321s76809 59755 0.27326 158 1050 0.33599 0.24105 28.3% 5.7 325sH78299 61087 0.3359925 0.24105 28.3% 5.8 326s78784 61550 0.27531 231 842 0.33599 0.24105 28.3% 5.8 330sH79225 61400 0.3349799 0.24105 28.0% 5.8 330s80075 62170 0.27675 290 694 0.33498 0.24105 28.0% 5.8 335sH80267 62255 0.3347949 0.24105 28.0% 5.8 335s81485 63362 0.27713 310 641 0.33479 0.24105 28.0% 5.8 340sH81494 63362 0.3347949 0.24105 28.0% 5.8 340sH81633 63486 0.3347758 0.24105 28.0% 5.8 340sH81769 63415 0.3343654 0.24105 27.9% 5.8 340s82370 63959 0.27726 320 638 0.33437 0.24105 27.9% 5.8 345sH82771 64141 0.3338842 0.24105 27.8% 5.8 345sH82869 64134 0.3336610 0.24105 27.8% 5.8 345s83795 65021 0.27921 374 522 0.33366 0.24105 27.8% 5.8 350sH83886 64999 0.3334210 0.24105 27.7% 5.8 350sH84584 65694 0.3334210 0.24105 27.7% 5.8 350s84975 66099 0.28012 417 416 0.33342 0.24105 27.7% 5.8 355sH85478 66468 0.3334089 0.24105 27.7% 5.8 355s86421 67432 0.28227 480 394 0.33341 0.24105 27.7% 5.8 360sH87418 67882 0.3325937 0.24105 27.5% 5.8 360s88349 68957 0.28160 538 296 0.33259 0.24105 27.5% 5.8 366sH88351 68957 0.3325937 0.24105 27.5% 5.8 366sH88421 68794 0.3321122 0.24105 27.4% 5.8 366sH89497 69824 0.3321122 0.24105 27.4% 5.8 366sH89499 69824 0.3321122 0.24105 27.4% 5.8 366s89544 69880 0.28179 564 289 0.33211 0.24105 27.4% 5.8 371sH89748 70024 0.3321122 0.24105 27.4% 5.8 371s91355 71561 0.28254 638 266 0.33211 0.24105 27.4% 5.9 376sH91813 71544 0.3312166 0.24105 27.2% 5.9 376s93071 72691 0.29520 699 215 0.33122 0.24105 27.2% 5.9 382s94549 74071 0.29758 764 177 0.33122 0.24105 27.2% 5.9 388s96307 75617 0.24464 51 1229 0.33122 0.24105 27.2% 5.9 393s98287 77475 0.25137 245 1104 0.33122 0.24105 27.2% 5.9 398s98983 78179 0.26315 359 917 0.33122 0.24105 27.2% 5.9 404s100364 79457 0.26504 477 729 0.33122 0.24105 27.2% 5.8 409s101882 80860 0.26903 614 575 0.33122 0.24105 27.2% 5.8 414s103283 82243 0.27082 733 429 0.33122 0.24105 27.2% 5.8 420sH103543 82070 0.3306340 0.24105 27.1% 5.8 420sH103758 82285 0.3306340 0.24105 27.1% 5.8 420s104633 83150 0.27221 851 321 0.33063 0.24105 27.1% 5.8 425sH104968 83231 0.3304686 0.24105 27.1% 5.8 425sH105952 84275 0.3304318 0.24105 27.0% 5.8 425s106708 85089 0.28549 1019 141 0.33043 0.24105 27.0% 5.8 431sH107379 85571 0.3301261 0.24105 27.0% 5.8 432s109208 87275 0.30339 1207 36 0.33013 0.24105 27.0% 5.8 437sH109594 87550 0.3301261 0.24105 27.0% 5.8 437s111037 88962 0.24906 173 1168 0.33013 0.24105 27.0% 5.8 442sH111050 88962 0.3301261 0.24105 27.0% 5.8 442sH111296 89148 0.3299165 0.24105 26.9% 5.8 442sH112193 90009 0.3298353 0.24105 26.9% 5.8 442s112697 90480 0.26851 331 977 0.32984 0.24105 26.9% 5.8 447sH112908 90467 0.3292711 0.24105 26.8% 5.8 447s113842 91266 0.27017 435 786 0.32927 0.24105 26.8% 5.8 453sH114159 91424 0.3292711 0.24105 26.8% 5.8 453s115588 92952 0.27436 594 598 0.32927 0.24105 26.8% 5.8 458sH115698 93060 0.3292711 0.24105 26.8% 5.8 458sH116316 93548 0.3290540 0.24105 26.7% 5.8 458s116553 93749 0.27704 703 450 0.32905 0.24105 26.7% 5.8 463s118337 95473 0.27857 851 319 0.32905 0.24105 26.7% 5.8 468sH119368 96330 0.3289081 0.24105 26.7% 5.8 468s120230 97185 0.29193 1013 132 0.32891 0.24105 26.7% 5.8 474sH120300 97185 0.3289081 0.24105 26.7% 5.8 474s121723 98673 0.24550 73 1228 0.32891 0.24105 26.7% 5.8 479sH121955 98793 0.3287024 0.24105 26.7% 5.8 479sH121971 98778 0.3286457 0.24105 26.7% 5.8 479sH122386 98591 0.3275818 0.24105 26.4% 5.8 479s123230 99405 0.24758 151 1205 0.32758 0.24105 26.4% 5.8 485sH123471 99472 0.3275738 0.24105 26.4% 5.8 485s124743 100824 0.25096 222 1102 0.32757 0.24105 26.4% 5.8 491sH124800 100824 0.3275738 0.24105 26.4% 5.8 491s126572 102593 0.25679 380 809 0.32757 0.24105 26.4% 5.8 496s127944 103954 0.26433 491 720 0.32757 0.24105 26.4% 5.8 501s129747 105702 0.28498 648 494 0.32757 0.24105 26.4% 5.7 506sH129765 105551 0.3272253 0.24105 26.3% 5.7 506sH130989 106549 0.3272000 0.24105 26.3% 5.7 506s131225 106786 0.29659 802 310 0.32720 0.24105 26.3% 5.7 512sH131300 106644 0.3268833 0.24105 26.3% 5.7 512sH131400 106644 0.3268833 0.24105 26.3% 5.7 512s133263 108487 0.24407 60 1235 0.32688 0.24105 26.3% 5.7 517s134371 109462 0.24596 98 1224 0.32688 0.24105 26.3% 5.7 522sH135641 110567 0.3268825 0.24105 26.3% 5.7 522s135772 110795 0.25042 213 1113 0.32688 0.24105 26.3% 5.7 527s137255 112288 0.25567 342 894 0.32688 0.24105 26.3% 5.7 532s138718 113700 0.27328 488 655 0.32688 0.24105 26.3% 5.7 538s140823 115790 0.29196 671 402 0.32688 0.24105 26.3% 5.7 543s142810 117611 0.31552 958 136 0.32688 0.24105 26.3% 5.7 549s144714 119335 0.24735 104 1213 0.32688 0.24105 26.3% 5.7 553sH145081 119653 0.3268825 0.24105 26.3% 5.7 553s145962 120444 0.26841 191 1162 0.32688 0.24105 26.3% 5.6 558sH146832 120960 0.3264049 0.24105 26.1% 5.6 558s147412 121545 0.28925 330 1027 0.32640 0.24105 26.1% 5.6 564s149341 123454 0.29208 519 676 0.32640 0.24105 26.1% 5.6 569s150991 125080 0.29321 628 512 0.32640 0.24105 26.1% 5.6 574s152268 126253 0.29720 738 462 0.32640 0.24105 26.1% 5.6 579s153423 127416 0.29789 794 391 0.32640 0.24105 26.1% 5.6 584s155301 129197 0.30159 972 221 0.32640 0.24105 26.1% 5.6 590sH155614 129077 0.3259716 0.24105 26.1% 5.6 590s156866 130291 0.32394 1047 160 0.32597 0.24105 26.1% 5.6 595sH157947 131306 0.3259716 0.24105 26.1% 5.6 595s158411 131741 0.24547 144 1205 0.32597 0.24105 26.1% 5.6 600sH159230 132407 0.3259716 0.24105 26.1% 5.6 600s160288 133487 0.26668 304 963 0.32597 0.24105 26.1% 5.6 605s161245 134417 0.27287 417 749 0.32597 0.24105 26.1% 5.6 610sH161924 134971 0.3259716 0.24105 26.1% 5.6 610sH162314 135042 0.3254982 0.24105 25.9% 5.6 610s162812 135586 0.29619 573 539 0.32550 0.24105 25.9% 5.6 615sH162900 135586 0.3254982 0.24105 25.9% 5.6 616sH163100 135586 0.3254970 0.24105 25.9% 5.6 616s165100 137707 0.32357 862 219 0.32550 0.24105 25.9% 5.6 621sH165773 138289 0.3254288 0.24105 25.9% 5.6 621sH166076 138199 0.3249900 0.24105 25.8% 5.6 621s166278 138389 0.24200 47 1241 0.32499 0.24105 25.8% 5.6 627sH167258 139235 0.3249631 0.24105 25.8% 5.6 627s167414 139360 0.24643 102 1221 0.32496 0.24105 25.8% 5.6 632sH167433 139360 0.3249631 0.24105 25.8% 5.6 632sH167590 139533 0.3249631 0.24105 25.8% 5.6 632s168702 140586 0.24836 189 1179 0.32496 0.24105 25.8% 5.6 637sH169298 140903 0.3248948 0.24105 25.8% 5.6 637sH169397 141063 0.3248554 0.24105 25.8% 5.6 637s170912 142504 0.32029 387 937 0.32486 0.24105 25.8% 5.6 642sH172624 144037 0.3248554 0.24105 25.8% 5.5 643s172914 144409 0.24774 133 1207 0.32486 0.24105 25.8% 5.5 648s174837 146340 0.29655 320 1016 0.32486 0.24105 25.8% 5.5 654s176513 147947 0.30209 484 726 0.32486 0.24105 25.8% 5.5 659s178551 149944 0.30664 672 558 0.32486 0.24105 25.8% 5.5 664sH179409 150608 0.3248554 0.24105 25.8% 5.5 664s179974 151248 0.31533 813 415 0.32486 0.24105 25.8% 5.5 669sH180227 151373 0.3248554 0.24105 25.8% 5.5 669s181270 152543 0.31877 939 271 0.32486 0.24105 25.8% 5.5 674sH181311 152219 0.3245494 0.24105 25.7% 5.5 674s181871 152737 0.32229 996 214 0.32455 0.24105 25.7% 5.5 679sH182130 152842 0.3245494 0.24105 25.7% 5.5 679sH182260 152972 0.3245494 0.24105 25.7% 5.5 679sH182978 153578 0.3244530 0.24105 25.7% 5.5 679s183339 153950 0.24778 126 1200 0.32445 0.24105 25.7% 5.5 684s184823 155364 0.24845 188 1174 0.32445 0.24105 25.7% 5.5 690s186908 157394 0.25979 366 962 0.32445 0.24105 25.7% 5.5 695sH187365 157570 0.3243848 0.24105 25.7% 5.5 695s188954 159285 0.26131 459 807 0.32438 0.24105 25.7% 5.5 701sH190223 160341 0.3242944 0.24105 25.7% 5.5 701s190672 160722 0.26383 606 633 0.32429 0.24105 25.7% 5.5 706s192274 162285 0.26736 735 478 0.32429 0.24105 25.7% 5.5 711s193653 163658 0.30584 862 322 0.32429 0.24105 25.7% 5.5 715sH193658 163658 0.3242944 0.24105 25.7% 5.5 715sH193665 163658 0.3242944 0.24105 25.7% 5.5 715sH193755 163180 0.3239186 0.24105 25.6% 5.5 715sH194033 163131 0.3237082 0.24105 25.5% 5.5 715s194381 163501 0.30884 927 256 0.32371 0.24105 25.5% 5.5 720sH195720 164570 0.3237082 0.24105 25.5% 5.5 720s195721 164692 0.32343 1045 38 0.32371 0.24105 25.5% 5.5 726s197859 166704 0.24973 203 1149 0.32371 0.24105 25.5% 5.5 731s199612 168319 0.25659 345 1009 0.32371 0.24105 25.5% 5.5 736s201384 170014 0.25944 534 693 0.32371 0.24105 25.5% 5.4 743s202306 170909 0.26371 651 590 0.32371 0.24105 25.5% 5.4 749s204166 172711 0.30288 846 355 0.32371 0.24105 25.5% 5.4 754s205786 174323 0.31150 980 194 0.32371 0.24105 25.5% 5.4 760s207162 175624 0.24567 92 1227 0.32371 0.24105 25.5% 5.4 765sH207301 175762 0.3237082 0.24105 25.5% 5.4 765s208796 177249 0.25039 231 1097 0.32371 0.24105 25.5% 5.4 771sH209156 176102 0.3227803 0.24105 25.3% 5.4 771s209987 176921 0.25945 336 921 0.32278 0.24105 25.3% 5.4 776s211355 178245 0.26105 450 747 0.32278 0.24105 25.3% 5.4 782s213129 179910 0.26344 595 590 0.32278 0.24105 25.3% 5.4 788sH213544 179988 0.3226444 0.24105 25.3% 5.4 788s215162 181589 0.26838 767 410 0.32264 0.24105 25.3% 5.4 793s216725 183113 0.27199 906 272 0.32264 0.24105 25.3% 5.4 799sH218430 183121 0.3218367 0.24105 25.1% 5.4 799s218712 183562 0.24822 166 1181 0.32184 0.24105 25.1% 5.4 804sH219093 183789 0.3218176 0.24105 25.1% 5.4 804sH219526 184099 0.3217544 0.24105 25.1% 5.4 804s220718 185323 0.25963 355 882 0.32175 0.24105 25.1% 5.4 810sH220733 185323 0.3217544 0.24105 25.1% 5.4 810sH220800 185323 0.3217544 0.24105 25.1% 5.4 810sH220987 185587 0.3217544 0.24105 25.1% 5.4 810sH221024 185544 0.3217353 0.24105 25.1% 5.4 810s222514 186898 0.26252 538 611 0.32174 0.24105 25.1% 5.4 816sH223697 187923 0.3217353 0.24105 25.1% 5.4 816sH224316 188437 0.3217353 0.24105 25.1% 5.4 816s224469 188749 0.26722 725 456 0.32174 0.24105 25.1% 5.4 821sH224600 188568 0.3216285 0.24105 25.1% 5.4 821s225810 189845 0.26877 857 314 0.32163 0.24105 25.1% 5.4 826s227806 191652 0.24795 149 1194 0.32163 0.24105 25.1% 5.4 831s229664 193403 0.26276 311 1012 0.32163 0.24105 25.1% 5.4 836s230825 194491 0.26492 448 764 0.32163 0.24105 25.1% 5.4 841s232461 196020 0.26632 524 643 0.32163 0.24105 25.1% 5.4 847sH232484 195118 0.3210901 0.24105 24.9% 5.4 847sH232661 194040 0.3203864 0.24105 24.8% 5.4 847s232941 194285 0.26691 564 549 0.32039 0.24105 24.8% 5.4 852sH233000 194285 0.3203864 0.24105 24.8% 5.4 852s234655 195933 0.27062 694 447 0.32039 0.24105 24.8% 5.4 857s236481 197538 0.27208 840 338 0.32039 0.24105 24.8% 5.4 862s237702 198697 0.28557 960 206 0.32039 0.24105 24.8% 5.4 867s239802 200743 0.24738 137 1202 0.32039 0.24105 24.8% 5.4 872s241537 202467 0.26482 299 1056 0.32039 0.24105 24.8% 5.4 877s243021 203872 0.26890 463 749 0.32039 0.24105 24.8% 5.4 883sH243029 203872 0.3203864 0.24105 24.8% 5.4 883sH243519 204365 0.3203864 0.24105 24.8% 5.4 883sH243810 203588 0.3198328 0.24105 24.6% 5.4 883s244384 204202 0.27013 553 641 0.31983 0.24105 24.6% 5.4 888sH245524 204122 0.3192583 0.24105 24.5% 5.4 888s245765 204376 0.29373 670 501 0.31926 0.24105 24.5% 5.4 893s247272 205865 0.29518 806 377 0.31926 0.24105 24.5% 5.4 898sH247421 205993 0.3192583 0.24105 24.5% 5.3 898sH247639 206195 0.3192583 0.24105 24.5% 5.3 898s248280 206837 0.30481 935 186 0.31926 0.24105 24.5% 5.3 903s249579 208123 0.24441 89 1231 0.31926 0.24105 24.5% 5.3 908s251043 209593 0.24862 211 1161 0.31926 0.24105 24.5% 5.3 914s252884 211374 0.26349 369 959 0.31926 0.24105 24.5% 5.3 919s254257 212682 0.26544 486 746 0.31926 0.24105 24.5% 5.3 924s255949 214392 0.26913 630 605 0.31926 0.24105 24.5% 5.3 929s257795 216112 0.28935 782 413 0.31926 0.24105 24.5% 5.3 935sH257940 214673 0.3186187 0.24105 24.3% 5.3 935s258531 215269 0.28977 851 342 0.31862 0.24105 24.3% 5.3 941sH258700 215269 0.3186187 0.24105 24.3% 5.3 941sH259620 215682 0.3184194 0.24105 24.3% 5.3 941s260514 216539 0.30691 1021 132 0.31842 0.24105 24.3% 5.3 946s262169 218173 0.24439 156 1198 0.31842 0.24105 24.3% 5.3 952s263613 219506 0.25007 282 977 0.31842 0.24105 24.3% 5.3 957s265725 221476 0.25163 360 909 0.31842 0.24105 24.3% 5.3 962sH266328 221599 0.3182764 0.24105 24.3% 5.3 962s267735 223013 0.26000 528 665 0.31828 0.24105 24.3% 5.3 968sH267966 223178 0.3182764 0.24105 24.3% 5.3 968s269544 224650 0.28929 694 442 0.31828 0.24105 24.3% 5.3 973s270957 226048 0.31466 897 207 0.31828 0.24114 24.2% 5.3 979s272499 227509 0.24565 121 1214 0.31828 0.24114 24.2% 5.3 984s273443 228388 0.24734 167 1179 0.31828 0.24114 24.2% 5.3 989sH273447 228388 0.3182764 0.24114 24.2% 5.3 989sH273466 228126 0.3181796 0.24114 24.2% 5.3 989sH274185 228753 0.3181623 0.24114 24.2% 5.3 989sH274216 227998 0.3178857 0.24114 24.1% 5.3 989s274316 228123 0.26281 225 1143 0.31789 0.24114 24.1% 5.3 995sH275610 229192 0.3178857 0.24114 24.1% 5.3 995s276510 230262 0.26847 410 914 0.31789 0.24114 24.1% 5.3 1000s278212 231957 0.27096 572 674 0.31789 0.24114 24.1% 5.3 1006s280425 234071 0.27713 761 496 0.31789 0.24114 24.1% 5.3 1011s281905 235497 0.29014 937 283 0.31789 0.24114 24.1% 5.3 1016s283859 237396 0.24391 108 1228 0.31789 0.24114 24.1% 5.3 1021s284928 238312 0.24671 159 1179 0.31789 0.24114 24.1% 5.3 1025s286392 239629 0.24156 76 1245 0.31789 0.24114 24.1% 5.3 1031s287642 240771 0.24591 129 1202 0.31789 0.24114 24.1% 5.3 1035s288987 242001 0.24722 188 1178 0.31789 0.24114 24.1% 5.3 1040s290187 243191 0.29119 302 1078 0.31789 0.24114 24.1% 5.3 1045s291023 244008 0.30830 416 894 0.31789 0.24114 24.1% 5.3 1051s292927 245898 0.31081 594 632 0.31789 0.24114 24.1% 5.3 1056s294704 247623 0.31414 759 402 0.31789 0.24114 24.1% 5.3 1061s296639 249421 cutoff 856 0.31789 0.24114 24.1% 5.3 1066s298170 250852 0.24591 134 1197 0.31789 0.24114 24.1% 5.3 1071s299283 251898 0.26930 224 1131 0.31789 0.24114 24.1% 5.3 1076s300247 252818 0.28836 317 1071 0.31789 0.24114 24.1% 5.3 1081s301918 254409 0.29038 471 809 0.31789 0.24114 24.1% 5.3 1087s303856 256181 0.29286 654 572 0.31789 0.24114 24.1% 5.3 1092s305379 257574 0.29714 799 430 0.31789 0.24114 24.1% 5.3 1097s307004 259002 0.31594 1008 218 0.31789 0.24114 24.1% 5.3 1103sH307030 258930 0.3178677 0.24114 24.1% 5.3 1103sH307803 259251 0.3177613 0.24114 24.1% 5.3 1103sH308103 258528 0.3174898 0.24114 24.0% 5.3 1103sH308228 258083 0.3173402 0.24114 24.0% 5.3 1103s308268 258208 0.24668 141 1179 0.31734 0.24114 24.0% 5.3 1108sH309654 259421 0.3173402 0.24114 24.0% 5.3 1108s309788 259707 0.30379 231 1136 0.31734 0.24114 24.0% 5.3 1114sH310691 260489 0.3173402 0.24114 24.0% 5.3 1114s311144 260947 0.30561 344 949 0.31734 0.24114 24.0% 5.3 1120sH312870 261575 0.3170701 0.24114 23.9% 5.3 1120s313028 261909 0.31254 498 741 0.31707 0.24114 23.9% 5.3 1126s315001 263687 0.31527 670 513 0.31707 0.24114 23.9% 5.3 1131sH316172 264422 0.3169818 0.24114 23.9% 5.3 1132s316907 265167 0.24182 75 1242 0.31698 0.24114 23.9% 5.3 1137sH317434 264773 0.3167483 0.24114 23.9% 5.3 1137s318442 265789 0.24556 107 1210 0.31675 0.24114 23.9% 5.3 1142s319865 267210 0.24672 132 1189 0.31675 0.24114 23.9% 5.3 1147sH320102 266691 0.3165412 0.24114 23.8% 5.3 1147s321151 267758 0.24926 157 1165 0.31654 0.24114 23.8% 5.3 1152s322678 269177 0.25281 179 1147 0.31654 0.24114 23.8% 5.3 1157sH323867 270206 0.3165412 0.24114 23.8% 5.3 1157s323991 270392 0.28510 190 1128 0.31654 0.24114 23.8% 5.3 1161sH324197 269928 0.3163332 0.24114 23.8% 5.3 1161sH324350 270040 0.3163138 0.24114 23.8% 5.3 1161s324479 270161 0.28596 200 1111 0.31631 0.24114 23.8% 5.3 1168sH324963 270499 0.3163138 0.24114 23.8% 5.3 1168s325940 271442 0.28831 227 1065 0.31631 0.24114 23.8% 5.3 1174sH326278 271728 0.3163138 0.24114 23.8% 5.3 1174s327118 272520 0.24428 112 1227 0.31631 0.24114 23.8% 5.3 1175s327662 273058 0.28887 250 1010 0.31631 0.24114 23.8% 5.3 1180s329110 274466 0.28965 281 936 0.31631 0.24114 23.8% 5.3 1186sH329874 274936 0.3162974 0.24114 23.8% 5.3 1186sH331093 274928 0.3159559 0.24114 23.7% 5.3 1186s331094 275115 0.29282 340 792 0.31596 0.24114 23.7% 5.3 1191sH331299 275316 0.3159559 0.24114 23.7% 5.3 1191sH331355 274546 0.3157543 0.24114 23.6% 5.3 1191sH331383 274242 0.3156761 0.24114 23.6% 5.3 1191s332050 274921 0.29348 361 738 0.31568 0.24114 23.6% 5.3 1197s333860 276568 0.29423 384 691 0.31568 0.24114 23.6% 5.3 1203s335365 278017 0.29770 408 629 0.31568 0.24114 23.6% 5.3 1209s336895 279413 0.29862 426 605 0.31568 0.24114 23.6% 5.3 1214sH336900 277353 0.3151259 0.24114 23.5% 5.3 1214s338134 278611 0.30015 442 587 0.31513 0.24114 23.5% 5.3 1220s339364 279705 0.30086 451 567 0.31513 0.24114 23.5% 5.3 1226sH339400 278457 0.3147486 0.24114 23.4% 5.3 1226s340985 280022 0.30257 470 546 0.31475 0.24114 23.4% 5.3 1232s342238 281210 0.30423 498 495 0.31475 0.24114 23.4% 5.3 1238s344238 282969 0.30541 523 450 0.31475 0.24114 23.4% 5.3 1245sH344300 282969 0.3147486 0.24114 23.4% 5.3 1245sH345052 282755 0.3145451 0.24114 23.3% 5.3 1245sH346013 281476 0.3139316 0.24114 23.2% 5.3 1245s346277 281766 0.30730 545 430 0.31393 0.24114 23.2% 5.3 1251s347925 283202 0.30921 569 361 0.31393 0.24114 23.2% 5.3 1257sH348000 283202 0.3139316 0.24114 23.2% 5.3 1257sH349076 282470 0.3134672 0.24114 23.1% 5.3 1257s349793 283266 0.31079 602 309 0.31347 0.24114 23.1% 5.3 1262s351129 284462 0.31256 638 254 0.31347 0.24114 23.1% 5.3 1269sH351834 282952 0.3128192 0.24114 22.9% 5.3 1269s352786 283882 0.24563 144 1190 0.31282 0.24114 22.9% 5.3 1274s354093 285086 0.27766 259 1087 0.31282 0.24114 22.9% 5.3 1280s355294 286280 0.27906 367 921 0.31282 0.24114 22.9% 5.3 1286sH355296 286280 0.3128192 0.24114 22.9% 5.3 1286sH355301 285185 0.3125152 0.24114 22.8% 5.3 1286s356303 286162 0.28038 474 792 0.31252 0.24114 22.8% 5.3 1292s358474 288204 0.30297 672 455 0.31252 0.24114 22.8% 5.3 1298sH359636 288529 0.3123130 0.24114 22.8% 5.3 1299s359976 288878 0.30456 799 344 0.31231 0.24114 22.8% 5.3 1306sH360000 288878 0.3123130 0.24114 22.8% 5.3 1306sH360750 289100 0.3121860 0.24114 22.8% 5.3 1306s361476 289809 0.30824 924 225 0.31219 0.24114 22.8% 5.3 1312s362976 291198 0.24417 125 1218 0.31219 0.24114 22.8% 5.3 1318sH364091 292264 0.3121860 0.24114 22.8% 5.3 1318s364460 292587 0.25028 228 1135 0.31219 0.24114 22.8% 5.3 1324s365729 293853 cutoff 567 0.31219 0.24114 22.8% 5.3 1325s366152 294209 0.25323 359 940 0.31219 0.24114 22.8% 5.3 1330s367925 295825 0.25549 498 630 0.31219 0.24114 22.8% 5.3 1337s369631 297366 0.26017 660 496 0.31219 0.24114 22.8% 5.3 1343s371365 298890 0.26144 820 379 0.31219 0.24114 22.8% 5.3 1349sH372158 298525 0.3118679 0.24114 22.7% 5.3 1349s372874 299248 0.26297 927 286 0.31187 0.24114 22.7% 5.3 1354s374212 300453 0.26408 938 259 0.31187 0.24114 22.7% 5.3 1360s376449 302468 0.26788 992 217 0.31187 0.24114 22.7% 5.3 1366s378206 303975 0.26959 1028 202 0.31187 0.24115 22.7% 5.3 1372sH378495 303549 0.3117132 0.24115 22.6% 5.3 1372s380342 305167 0.24185 109 1234 0.31171 0.24115 22.6% 5.3 1378sH380352 305167 0.3117132 0.24115 22.6% 5.3 1378sH380354 305167 0.3117132 0.24115 22.6% 5.3 1378sH380368 305167 0.3117132 0.24115 22.6% 5.3 1378sH380507 305237 0.3117132 0.24115 22.6% 5.3 1379sH380829 304692 0.3114761 0.24115 22.6% 5.3 1379s381150 305029 0.24349 165 1181 0.31148 0.24115 22.6% 5.3 1385sH381804 305493 0.3114761 0.24115 22.6% 5.3 1385s382962 306572 0.31108 288 1039 0.31148 0.24115 22.6% 5.3 1390s384517 308016 0.24647 181 1175 0.31148 0.24115 22.6% 5.3 1396s386629 309922 0.30972 357 956 0.31148 0.24115 22.6% 5.3 1401s388490 311677 0.24123 90 1243 0.31148 0.24115 22.6% 5.3 1407s389689 312868 0.24452 137 1211 0.31148 0.24115 22.6% 5.3 1413s390981 314115 0.27074 245 1100 0.31148 0.24115 22.6% 5.3 1418s392521 315665 0.27521 365 937 0.31148 0.24115 22.6% 5.3 1424s394681 317683 0.27816 545 596 0.31148 0.24115 22.6% 5.3 1430sH395131 317923 0.3114149 0.24115 22.6% 5.3 1430sH395507 318222 0.3113902 0.24115 22.6% 5.3 1430s395628 318358 0.27845 584 576 0.31139 0.24115 22.6% 5.3 1437sH396392 319036 0.3113902 0.24115 22.6% 5.3 1437s397677 320295 0.28268 757 458 0.31139 0.24115 22.6% 5.3 1442s399198 321746 0.28405 881 334 0.31139 0.24115 22.6% 5.3 1448s400486 322999 0.24848 178 1176 0.31139 0.24119 22.5% 5.3 1455s401866 324311 0.24516 146 1195 0.31139 0.24119 22.5% 5.3 1461s403586 325971 0.29283 281 1086 0.31139 0.24119 22.5% 5.3 1467s405066 327377 0.30129 396 930 0.31139 0.24119 22.5% 5.3 1474sH405198 326945 0.3112627 0.24119 22.5% 5.3 1474s406001 327738 0.30237 472 775 0.31126 0.24119 22.5% 5.3 1480sH406200 327738 0.3112627 0.24119 22.5% 5.3 1480s408401 330051 0.30557 672 513 0.31126 0.24123 22.5% 5.3 1487s410692 332169 0.25121 184 1177 0.31126 0.24124 22.5% 5.3 1492s411897 333154 0.28110 259 1108 0.31126 0.24124 22.5% 5.3 1498s413089 334325 0.28233 360 952 0.31126 0.24124 22.5% 5.3 1504s414486 335671 0.28396 463 753 0.31126 0.24124 22.5% 5.3 1510sH414600 335671 0.3112627 0.24124 22.5% 5.3 1510sH414930 335901 0.3112627 0.24124 22.5% 5.3 1510s415966 337124 0.28547 564 622 0.31126 0.24124 22.5% 5.3 1517sH416939 337609 0.3111587 0.24124 22.5% 5.3 1517s417580 338300 0.28754 691 472 0.31116 0.24124 22.5% 5.3 1523s419075 339620 0.28878 791 384 0.31116 0.24124 22.5% 5.3 1529s420683 341112 0.30210 873 306 0.31116 0.24124 22.5% 5.3 1536sH420778 341174 0.3111587 0.24124 22.5% 5.3 1536s421751 342124 0.30244 925 278 0.31116 0.24124 22.5% 5.3 1543s423430 343659 0.30549 956 252 0.31116 0.24124 22.5% 5.3 1550s425579 345679 0.30809 990 192 0.31116 0.24124 22.5% 5.3 1556sH427063 346888 0.3111587 0.24124 22.5% 5.3 1556s427186 347125 0.30928 1031 158 0.31116 0.24124 22.5% 5.3 1562s428521 348401 0.30979 1132 49 0.31116 0.24128 22.5% 5.3 1568s430567 350330 0.30550 235 1029 0.31116 0.24128 22.5% 5.3 1574sH430734 349202 0.3108687 0.24128 22.4% 5.3 1574s432137 350454 0.31001 369 794 0.31087 0.24128 22.4% 5.3 1581s434067 352238 0.28623 223 1120 0.31087 0.24128 22.4% 5.3 1588sH434468 352501 0.3108687 0.24128 22.4% 5.3 1588sH434636 352445 0.3107910 0.24128 22.4% 5.3 1588s435644 353420 0.28975 357 925 0.31079 0.24128 22.4% 5.3 1595sH436277 354029 0.3107910 0.24128 22.4% 5.3 1595sH436699 354363 0.3107910 0.24128 22.4% 5.3 1595s437356 355008 0.29215 514 669 0.31079 0.24128 22.4% 5.3 1601s438969 356487 0.29720 659 540 0.31079 0.24128 22.4% 5.3 1608s440640 358046 0.29914 791 430 0.31079 0.24128 22.4% 5.3 1614s442069 359439 0.27030 206 1138 0.31079 0.24128 22.4% 5.3 1615s442391 359738 0.30335 952 243 0.31079 0.24128 22.4% 5.3 1621sH442616 359772 0.3107666 0.24128 22.4% 5.3 1621sH442618 359772 0.3107666 0.24128 22.4% 5.3 1621s443700 360843 0.24494 127 1213 0.31077 0.24128 22.4% 5.3 1628sH444406 360906 0.3106543 0.24128 22.3% 5.3 1628s445098 361498 0.25467 228 1128 0.31065 0.24128 22.3% 5.3 1634sH445612 361542 0.3105678 0.24128 22.3% 5.3 1634s446334 362304 0.27387 331 1045 0.31057 0.24128 22.3% 5.3 1641s448326 364230 0.27606 498 765 0.31057 0.24128 22.3% 5.3 1648sH448502 364403 0.3105678 0.24128 22.3% 5.3 1648s450468 366192 0.27890 673 580 0.31057 0.24128 22.3% 5.3 1654s452048 367708 0.28292 806 429 0.31057 0.24128 22.3% 5.3 1661sH452219 366923 0.3103777 0.24128 22.3% 5.3 1661sH452226 366859 0.3103641 0.24128 22.3% 5.3 1661s453142 367631 0.28489 932 294 0.31036 0.24128 22.3% 5.3 1668sH453200 367631 0.3103641 0.24128 22.3% 5.3 1669s455327 369435 0.24467 128 1213 0.31036 0.24128 22.3% 5.3 1675sH456786 369956 0.3102121 0.24128 22.2% 5.3 1675s457209 370514 0.25142 302 1002 0.31021 0.24128 22.2% 5.3 1681s459385 372526 0.25421 493 641 0.31021 0.24128 22.2% 5.3 1686sH460164 372778 0.3101131 0.24128 22.2% 5.3 1686sH460728 372791 0.3100348 0.24128 22.2% 5.3 1686s460745 372921 0.26614 609 489 0.31003 0.24128 22.2% 5.3 1692sH461165 373162 0.3100348 0.24128 22.2% 5.3 1692sH462509 370356 0.3092193 0.24128 22.0% 5.3 1692s462724 370609 0.27071 765 375 0.30922 0.24128 22.0% 5.3 1698sH462800 370609 0.3092193 0.24128 22.0% 5.3 1698s464758 372408 0.27471 942 243 0.30922 0.24128 22.0% 5.3 1704s466475 373744 0.30156 1085 57 0.30922 0.24128 22.0% 5.3 1710sH466988 373618 0.3091204 0.24128 21.9% 5.3 1710sH467940 374258 0.3091023 0.24128 21.9% 5.3 1710s468067 374514 0.24177 92 1240 0.30910 0.24128 21.9% 5.3 1716s469682 376073 0.24857 226 1124 0.30910 0.24128 21.9% 5.3 1722sH470009 376384 0.3091023 0.24128 21.9% 5.3 1722sH470273 376606 0.3091023 0.24128 21.9% 5.3 1723s470847 377184 0.25014 345 918 0.30910 0.24128 21.9% 5.3 1729sH471000 375532 0.3087740 0.24128 21.9% 5.3 1729s472648 377170 0.30302 498 676 0.30877 0.24128 21.9% 5.3 1735sH474112 378416 0.3087480 0.24128 21.9% 5.3 1735s474213 378536 0.24266 92 1236 0.30875 0.24128 21.9% 5.3 1741s476147 380428 0.24450 122 1213 0.30875 0.24128 21.9% 5.3 1747s477432 381625 0.24570 139 1198 0.30875 0.24128 21.9% 5.3 1752s479073 383133 0.24920 159 1175 0.30875 0.24128 21.9% 5.3 1758s480469 384497 0.25523 186 1142 0.30875 0.24128 21.9% 5.3 1763s481907 385873 0.25615 200 1112 0.30875 0.24128 21.9% 5.3 1769s483739 387675 0.25720 218 1087 0.30875 0.24128 21.9% 5.3 1775s485638 389451 0.25811 255 994 0.30875 0.24128 21.9% 5.3 1780s486481 390271 0.25859 273 952 0.30875 0.24128 21.9% 5.3 1785s487972 391528 0.25913 294 895 0.30875 0.24128 21.9% 5.3 1792s489389 392740 0.25920 303 883 0.30875 0.24128 21.9% 5.3 1797s490805 393946 0.26017 327 828 0.30875 0.24128 21.9% 5.3 1803sH492526 395510 0.3087480 0.24128 21.9% 5.3 1803s492965 395998 0.26132 353 785 0.30875 0.24128 21.9% 5.3 1809s494084 397067 0.26210 366 760 0.30875 0.24128 21.9% 5.3 1814s494318 397297 0.25609 512 648 0.30875 0.24128 21.9% 5.3 1815s495572 398483 0.26267 386 714 0.30875 0.24128 21.9% 5.3 1821sH495958 398743 0.3087480 0.24128 21.9% 5.3 1821s497208 399992 0.26435 413 654 0.30875 0.24128 21.9% 5.3 1827sH497844 400520 0.3087480 0.24128 21.9% 5.3 1827s499374 402003 0.26528 441 622 0.30875 0.24128 21.9% 5.3 1833s500895 403443 0.26736 470 587 0.30875 0.24128 21.9% 5.3 1840sH501046 402808 0.3085771 0.24128 21.8% 5.3 1840s501868 403616 0.26738 477 583 0.30858 0.24128 21.8% 5.3 1846sH502399 404009 0.3085771 0.24128 21.8% 5.3 1846sH502532 402929 0.3083261 0.24128 21.7% 5.3 1846s503564 403930 0.27022 508 506 0.30833 0.24128 21.7% 5.3 1853s505508 405733 0.27231 535 449 0.30833 0.24128 21.7% 5.3 1859s507770 407670 0.28380 566 413 0.30833 0.24128 21.7% 5.3 1864s509257 409064 0.28638 584 376 0.30833 0.24128 21.7% 5.3 1870s510985 410706 0.28951 615 337 0.30833 0.24128 21.7% 5.3 1877s512137 411796 cutoff 637 0.30833 0.24128 21.7% 5.3 1884sH512364 411287 0.3082065 0.24128 21.7% 5.3 1884s514205 413091 0.29289 670 277 0.30821 0.24128 21.7% 5.3 1889s514775 413547 0.30534 597 599 0.30821 0.24128 21.7% 5.3 1890sH515066 413724 0.3082065 0.24128 21.7% 5.3 1890s516501 415185 0.29634 708 219 0.30821 0.24128 21.7% 5.3 1896sH516733 415279 0.3081855 0.24128 21.7% 5.3 1896s517993 416565 0.29938 724 205 0.30819 0.24128 21.7% 5.3 1902sH518774 417283 0.3081855 0.24128 21.7% 5.3 1902s519025 417539 0.30140 737 200 0.30819 0.24128 21.7% 5.3 1908s520637 419050 0.30622 764 172 0.30819 0.24128 21.7% 5.3 1914s522479 420756 0.24388 122 1221 0.30819 0.24129 21.7% 5.3 1920sH522583 420298 0.3080921 0.24129 21.7% 5.3 1920s523928 421586 0.24141 92 1242 0.30809 0.24129 21.7% 5.3 1926sH524000 420390 0.3078500 0.24129 21.6% 5.3 1926s525673 422029 0.30095 211 1129 0.30785 0.24129 21.6% 5.3 1931sH525828 422156 0.3078500 0.24129 21.6% 5.3 1931s527143 423484 0.30494 339 943 0.30785 0.24129 21.6% 5.3 1937s528983 425273 0.30760 497 682 0.30785 0.24129 21.6% 5.3 1944sH529237 422269 0.3072957 0.24129 21.5% 5.3 1944s529704 422729 0.24194 102 1240 0.30730 0.24129 21.5% 5.3 1949sH530079 422895 0.3072957 0.24129 21.5% 5.3 1949s531733 424595 0.29452 456 754 0.30730 0.24129 21.5% 5.3 1950s531930 424804 0.24760 133 1198 0.30730 0.24129 21.5% 5.3 1956s533638 426433 0.26677 295 1062 0.30730 0.24129 21.5% 5.3 1961s535467 428163 0.27022 458 757 0.30730 0.24129 21.5% 5.3 1967s536974 429520 0.27212 592 571 0.30730 0.24129 21.5% 5.3 1972s538097 430585 0.27388 719 476 0.30730 0.24129 21.5% 5.3 1978s539345 431770 0.27710 823 393 0.30730 0.24129 21.5% 5.3 1984s541082 433407 0.28930 979 218 0.30730 0.24129 21.5% 5.3 1990s541897 434166 0.24299 99 1234 0.30730 0.24129 21.5% 5.3 1996s543145 435407 0.24712 203 1111 0.30730 0.24129 21.5% 5.3 2002s545017 437244 0.25472 359 837 0.30730 0.24129 21.5% 5.3 2009s546863 438981 0.26206 543 559 0.30730 0.24129 21.5% 5.3 2014sH548083 439085 0.3071268 0.24129 21.4% 5.3 2015s548522 439489 0.29808 726 326 0.30713 0.24130 21.4% 5.3 2021sH550347 436253 0.3063850 0.24130 21.2% 5.3 2021s550706 436820 0.24707 152 1181 0.30639 0.24130 21.2% 5.3 2026sH550900 436820 0.3063850 0.24130 21.2% 5.3 2026s552593 438614 0.28213 347 1021 0.30639 0.24130 21.2% 5.3 2033sH552595 438614 0.3063850 0.24130 21.2% 5.3 2033sH553021 438595 0.3063071 0.24130 21.2% 5.3 2033sH553256 436717 0.3059901 0.24130 21.1% 5.3 2033s553366 436823 0.28385 455 832 0.30599 0.24130 21.1% 5.3 2038sH554064 437363 0.3059901 0.24130 21.1% 5.3 2039sH554700 437331 0.3058796 0.24130 21.1% 5.3 2039s554904 437564 0.28587 590 618 0.30588 0.24130 21.1% 5.3 2044sH555525 437477 0.3057817 0.24130 21.1% 5.3 2044s555626 437665 0.27747 667 586 0.30578 0.24130 21.1% 5.3 2045s556782 438803 0.29075 787 433 0.30578 0.24130 21.1% 5.3 2050s558274 440150 0.30122 1005 229 0.30578 0.24130 21.1% 5.3 2056s559546 441272 0.24164 92 1243 0.30578 0.24130 21.1% 5.3 2060sH559619 441327 0.3057817 0.24130 21.1% 5.3 2061s560381 442092 0.24550 147 1191 0.30578 0.24130 21.1% 5.3 2067s561774 443449 0.25140 267 1017 0.30578 0.24130 21.1% 5.3 2073s563398 444942 0.25326 394 788 0.30578 0.24130 21.1% 5.3 2079s564634 446069 0.25444 497 611 0.30578 0.24130 21.1% 5.3 2086s566638 447893 0.25698 664 412 0.30578 0.24130 21.1% 5.3 2093s568694 449657 0.27165 827 307 0.30578 0.24130 21.1% 5.3 2099s570078 450960 0.28275 934 143 0.30578 0.24130 21.1% 5.3 2106s571478 452314 0.24629 155 1169 0.30578 0.24131 21.1% 5.3 2111s572954 453765 0.24946 165 1176 0.30578 0.24131 21.1% 5.3 2118s574718 455466 0.28722 312 992 0.30578 0.24131 21.1% 5.3 2125s576914 457630 0.29168 495 695 0.30578 0.24131 21.1% 5.3 2132s579141 459802 0.29670 682 467 0.30578 0.24131 21.1% 5.3 2139sH580229 460547 0.3057487 0.24131 21.1% 5.3 2139s580798 461092 0.29963 842 287 0.30575 0.24131 21.1% 5.3 2145sH581455 461661 0.3057487 0.24131 21.1% 5.3 2145s581643 461873 0.24165 96 1243 0.30575 0.24131 21.1% 5.3 2151s583433 463526 0.24603 109 1222 0.30575 0.24131 21.1% 5.3 2158s585404 465397 0.29702 277 1076 0.30575 0.24131 21.1% 5.3 2164s

I tried changing the MIPFocus but does not seem to give me the expected results.

I would really appreciate if you could give me some suggestions over here to improve the performance.

• Gurobi Staff

Dear Gayan,

Could you provide some more information about the problem you are trying to solve? Is it, e.g., a parameter estimation problem? What does the objective and the constraints look like? Do you provide variable bounds for all variables participating in nonlinear terms in your problem?

Did you try to reformulate your problem in order to reduce the number of nonlinearities? For example, instead of writing $$x_1\cdot x_2 + x_1 \cdot x_3$$, you could introduce an auxiliary variable $$w_1$$ and reformulate it as $$x_1 \cdot w_1$$ and add the linear equality constraint $$w_1 = x_2 + x_3$$. This reduces the number of nonlinear term from 2 to 1 in this simple example.

Best regards,
Jaromił

Hi Jaromil,

The objective of the optimisation problem is in the quadratic form

objective : x^{T}Qx + u^{T}Ru %% quadraticwhereu \in {0,1,2,3,4} %% makes the problem MIQP%% constraints for u and xu_{min} \leq u leq u_{max}x_{min} \leq x leq x_{max}in addition to the integer constraint for u

By the way sorry for the inconvenience caused by LaTeX formulation, and I do not how to use the LaTeX formatting here.

Thank you.

• Gurobi Staff

Hi Gayan,

So the integer variables $$u$$ are independent of the continuous variables $$x$$. You could try reformulating your problem with binaries and then linearize the multiplication of two binaries since $$x^2 = x$$ for $$x \in \{0,1\}$$ and $$x \cdot y = z$$ can be reformulated exactly by

$$z \leq x$$
$$z \leq y$$
$$x+y \leq z +1.$$

The above follows directly from the McCormick envelope and the fact that $$x,y \in \{0,1\}$$.

You could also try reducing the number of nonlinear terms by reformulation as proposed in my previous comment. This could have a significant impact on solver performance.

Your problem does not have any constraints. Is it possible to add (in best case linear) constraints to shrink the feasible region of the problem?

Please don't forget that your non-convex MIQP has 2000 continuous and 4000 binary variables which is already considered very large for this problem type even without any constraints.

You can find a guide to our Support Portal here.

Best regards,
Jaromił

Hi Jaromil,

Sorry for being late to reply.

I think its better to give a full picture of the mathematical formulation as I was unable to convey the message properly.

Here is a very brief formulation of the optimisation problem,

\begin{align} \min_{u} \quad (x_{k}&-x_{ref})^{T}Q(x_{k}-x_{ref}) + (P_{k}-P_{ref})^{T}R(P_{k}-P_{ref}) \\ \text{s.t. } x_{k+1}&=Ax_{k}+Bu_{k} \\ P_{k} &= \mathbb{I}^{T}u_{k}\\ u_{k} &\in \{0,1,2,3,4\}\\ \underline{x}&\leq x_{k}\leq \overline{x} \end{align}

Q and R are positive definite matrices,

I developed the optimisation problem in YALMIP in MATLAB. In any case, you need to have a look at it, I am happy to share the MWE with you.

I would really appreciate if you could give me your suggestions based on the above formulation.

Kind regards

Gayan

• Gurobi Staff

Hi Gayan,

Thank you for the clarification. It seems like the lower bound (dual bound/BestBd) is already very good as it does not move much, while the incumbent is being improved very often over during the optimization. Do you have any information or a good guess about the optimal solution point which you could provide to Gurobi as explained in our article on MIP starts? This could help a lot. What is your expected MIPGap and run time for this kind of problem?

Do you have any additional information about the problem which you could feed into your problem formulation in order to tighten the feasible set, e.g., something like at timestep $$\texttt{i}$$ you know or you expect that some variables have to be 0 or similar.

To me, this problem formulation looks very similar to parameter estimation problems, which are known for their complexity. You could try reducing the size of your problem to see whether smaller problems are solvable in the time you desire and try building up from there.

Best regards,
Jaromił

Hi Jaromil,

Thank you for your comments and suggestions. MIP starts seems interesting but I need to make look into a possible feasible solution first.

At the same time I am not sure whether I have any additional information to tighten the feasible set.

Although I am aware of the fact that each optimisation process should be completeted within 60seconds (1 minute), still I have a certain doubt on the MIPGap that I should allow? Is there any rule of thumb sort of an approach to set the MIPGap?

I referred to the MIPGap article on Gurobi on how to set the parameters, but I am not sure about the value I should set. For example, whether 1e-2 is bad compared to the default 1e-4 or else whether I should avoid setting 1e-1 sort of value ?

At the same time, I tried the following approach for setting parameters in Gurobi with YALMIP as follows,

options = sdpsettings('verbose',2, 'solver', 'gurobi', 'gurobi.MIPFocus',1, 'gurobi.IterationLimit',100000);

I would really appreciate if you could give me some hints on the following.

1) Is it a bad approach to set an IterationLimit for the optimisation problem considering the convergence of it ? If No, does it
need to be a trial and error approach on setting the simplex iteration limit ?

2) I tried to perform the optimisation problem in the High Performance Computing (HPC) facility. As far as I know, PBS job allocation sort of a process is followed to execute a certain operation at the HPC. Initially I used ncpus =8 and after a while, an error message appeared

PBS: job killed: ncpus xxxx exceeded limit 8 (sum)

What I would like to know is  do I need to manually set Threads in Gurobi if I am performing optimisation on an HPC ? Does it affect the convergence of the problem ?

Sorry for the whole bunch of questions over here :(

Thank you.

• Gurobi Staff

Hi Gayan,

Regarding the MIPGap setting, it really depends on your application. Do you require the best (or at least a very good) solution or is it OK for your application to proceed with just a "good" solution. You are the one to define what is a "good" and a "very good" solution as you have the knowledge about your model and application. There is always the trade-off between solution quality and time. You state that your optimization problem should be solved within 60 seconds and the gap you reach at that time in your LOG file is 40%, so a MIPGap of 0.4. You could ask yourself if this is enough or if a solution point with lower objective value is required. Since your problem has rather small objective values, it could be useful to use the MIPGapAbs parameter. With that, you can terminate when a specific absolute difference between the best found solution and the lower bound is reached.

1) Setting the IterationLimit can work in some cases but it often boils down to a trial and error approach. It is better to set a TimeLimit instead.

2) Gurobi in general uses all cores of a given machine unless you specify the Threads to a fixed value. It seems to be the case with your job that the machine has more than 8 cores and Gurobi tries to use more than 8. Thus, the system kills your job as you only asked for a maximum of 8 CPUs. Setting the Threads parameter may have an impact on the convergence of the problem. In general one tends to say that more Threads are better but 8 Threads should be a good amount for most problems.

I hope the above could help.

Best regards,
Jaromił

Dear Jaromil,

Thank you very much for your useful suggestions.

I will follow them and update in the forum in case I face the same issue.

Cheers.