The MOSEK Python optimizer API manual Version 7.0 (Revision 141)
Optimizer API for Python - Documentation - Mosek Optimizer API for Python - Documentation - Mosek
538 APPENDIX C. RESPONSE CODES rescode.trm max time The optimizer terminated at the maximum amount of time. rescode.trm mio near abs gap The mixed-integer optimizer terminated because the near optimal absolute gap tolerance was satisfied. rescode.trm mio near rel gap The mixed-integer optimizer terminated because the near optimal relative gap tolerance was satisfied. rescode.trm mio num branches The mixed-integer optimizer terminated as to the maximum number of branches was reached. rescode.trm mio num relaxs The mixed-integer optimizer terminated as the maximum number of relaxations was reached. rescode.trm num max num int solutions The mixed-integer optimizer terminated as the maximum number of feasible solutions was reached. rescode.trm numerical problem The optimizer terminated due to numerical problems. rescode.trm objective range The optimizer terminated on the bound of the objective range. rescode.trm stall The optimizer is terminated due to slow progress. Stalling means that numerical problems prevent the optimizer from making reasonable progress and that it make no sense to continue. In many cases this happens if the problem is badly scaled or otherwise ill-conditioned. There is no guarantee that the solution will be (near) feasible or near optimal. However, often stalling happens near the optimum, and the returned solution may be of good quality. Therefore, it is recommended to check the status of then solution. If the solution near optimal the solution is most likely good enough for most practical purposes. Please note that if a linear optimization problem is solved using the interior-point optimizer with basis identification turned on, the returned basic solution likely to have high accuracy, even though the optimizer stalled. Some common causes of stalling are a) badly scaled models, b) near feasible or near infeasible problems and c) a non-convex problems. Case c) is only relevant for general non-linear problems. It is not possible in general for MOSEK to check if a specific problems is convex since such a check would be NP hard in itself. This implies that care should be taken when solving problems involving general user defined functions. rescode.trm user callback The optimizer terminated due to the return of the user-defined call-back function.
539 rescode.wrn ana almost int bounds This warning is issued by the problem analyzer if a constraint is bound nearly integral. rescode.wrn ana c zero This warning is issued by the problem analyzer, if the coefficients in the linear part of the objective are all zero. rescode.wrn ana close bounds This warning is issued by problem analyzer, if ranged constraints or variables with very close upper and lower bounds are detected. One should consider treating such constraints as equalities and such variables as constants. rescode.wrn ana empty cols This warning is issued by the problem analyzer, if columns, in which all coefficients are zero, are found. rescode.wrn ana large bounds This warning is issued by the problem analyzer, if one or more constraint or variable bounds are very large. One should consider omitting these bounds entirely by setting them to +inf or -inf. rescode.wrn construct invalid sol itg The intial value for one or more of the integer variables is not feasible. rescode.wrn construct no sol itg The construct solution requires an integer solution. rescode.wrn construct solution infeas After fixing the integer variables at the suggested values then the problem is infeasible. rescode.wrn dropped nz qobj One or more non-zero elements were dropped in the Q matrix in the objective. rescode.wrn duplicate barvariable names Two barvariable names are identical. rescode.wrn duplicate cone names Two cone names are identical. rescode.wrn duplicate constraint names Two constraint names are identical. rescode.wrn duplicate variable names Two variable names are identical. rescode.wrn eliminator space The eliminator is skipped at least once due to lack of space.
- Page 509 and 510: B.2. IPARAM: INTEGER PARAMETERS 487
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- Page 525 and 526: B.2. IPARAM: INTEGER PARAMETERS 503
- Page 527 and 528: B.3. SPARAM: STRING PARAMETER TYPES
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- Page 535 and 536: B.3. SPARAM: STRING PARAMETER TYPES
- Page 537 and 538: Appendix C Response codes Response
- Page 539 and 540: 517 rescode.err con q not psd The q
- Page 541 and 542: 519 rescode.err global inv conic pr
- Page 543 and 544: 521 rescode.err inv markj Invalid v
- Page 545 and 546: 523 rescode.err invalid format type
- Page 547 and 548: 525 rescode.err license no server s
- Page 549 and 550: 527 rescode.err mio no optimizer No
- Page 551 and 552: 529 rescode.err name max len A name
- Page 553 and 554: 531 rescode.err numconlim Maximum n
- Page 555 and 556: 533 rescode.err qcon upper triangle
- Page 557 and 558: 535 rescode.err sym mat invalid col
- Page 559: 537 rescode.err user nlo func The u
- Page 563 and 564: 541 rescode.wrn mio infeasible fina
- Page 565 and 566: 543 rescode.wrn write discarded cfi
- Page 567 and 568: Appendix D API constants D.1 Constr
- Page 569 and 570: D.5. PROGRESS CALL-BACK CODES 547 c
- Page 571 and 572: D.5. PROGRESS CALL-BACK CODES 549 c
- Page 573 and 574: D.5. PROGRESS CALL-BACK CODES 551 c
- Page 575 and 576: D.5. PROGRESS CALL-BACK CODES 553 c
- Page 577 and 578: D.6. TYPES OF CONVEXITY CHECKS. 555
- Page 579 and 580: D.10. DOUBLE INFORMATION ITEMS 557
- Page 581 and 582: D.10. DOUBLE INFORMATION ITEMS 559
- Page 583 and 584: D.11. FEASIBILITY REPAIR TYPES 561
- Page 585 and 586: D.13. INTEGER INFORMATION ITEMS. 56
- Page 587 and 588: D.13. INTEGER INFORMATION ITEMS. 56
- Page 589 and 590: D.13. INTEGER INFORMATION ITEMS. 56
- Page 591 and 592: D.16. INPUT/OUTPUT MODES 569 intpnt
- Page 593 and 594: D.20. CONTINUOUS MIXED-INTEGER SOLU
- Page 595 and 596: D.26. OBJECTIVE SENSE TYPES 573 D.2
- Page 597 and 598: D.31. PRESOLVE METHOD. 575 paramete
- Page 599 and 600: D.35. RESPONSE CODE TYPE 577 D.35 R
- Page 601 and 602: D.42. PROBLEM REFORMULATION. 579 si
- Page 603 and 604: D.46. SOLUTION TYPES 581 solsta.dua
- Page 605 and 606: D.50. STREAM TYPES 583 startpointty
- Page 607 and 608: Appendix E Troubleshooting When cre
- Page 609 and 610: Appendix F Mosek file formats MOSEK
538 APPENDIX C. RESPONSE CODES<br />
rescode.trm max time<br />
<strong>The</strong> <strong>optimizer</strong> terminated at the maximum amount of time.<br />
rescode.trm mio near abs gap<br />
<strong>The</strong> mixed-integer <strong>optimizer</strong> terminated because the near optimal absolute gap tolerance was<br />
satisfied.<br />
rescode.trm mio near rel gap<br />
<strong>The</strong> mixed-integer <strong>optimizer</strong> terminated because the near optimal relative gap tolerance was<br />
satisfied.<br />
rescode.trm mio num branches<br />
<strong>The</strong> mixed-integer <strong>optimizer</strong> terminated as to the maximum number of branches was reached.<br />
rescode.trm mio num relaxs<br />
<strong>The</strong> mixed-integer <strong>optimizer</strong> terminated as the maximum number of relaxations was reached.<br />
rescode.trm num max num int solutions<br />
<strong>The</strong> mixed-integer <strong>optimizer</strong> terminated as the maximum number of feasible solutions was<br />
reached.<br />
rescode.trm numerical problem<br />
<strong>The</strong> <strong>optimizer</strong> terminated due to numerical problems.<br />
rescode.trm objective range<br />
<strong>The</strong> <strong>optimizer</strong> terminated on the bound of the objective range.<br />
rescode.trm stall<br />
<strong>The</strong> <strong>optimizer</strong> is terminated due to slow progress.<br />
Stalling means that numerical problems prevent the <strong>optimizer</strong> from making reasonable progress<br />
and that it make no sense to continue. In many cases this happens if the problem is badly scaled<br />
or otherwise ill-conditioned. <strong>The</strong>re is no guarantee that the solution will be (near) feasible or<br />
near optimal. However, often stalling happens near the optimum, and the returned solution may<br />
be of good quality. <strong>The</strong>refore, it is recommended to check the status of then solution. If the<br />
solution near optimal the solution is most likely good enough for most practical purposes.<br />
Please note that if a linear optimization problem is solved using the interior-point <strong>optimizer</strong><br />
with basis identification turned on, the returned basic solution likely to have high accuracy, even<br />
though the <strong>optimizer</strong> stalled.<br />
Some common causes of stalling are a) badly scaled models, b) near feasible or near infeasible<br />
problems and c) a non-convex problems. Case c) is only relevant for general non-linear problems.<br />
It is not possible in general for <strong>MOSEK</strong> to check if a specific problems is convex since such a<br />
check would be NP hard in itself. This implies that care should be taken when solving problems<br />
involving general user defined functions.<br />
rescode.trm user callback<br />
<strong>The</strong> <strong>optimizer</strong> terminated due to the return of the user-defined call-back function.