The MOSEK Python optimizer API manual Version 7.0 (Revision 141)

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566 APPENDIX D. API CONSTANTS iinfitem.sim dual inf iter The number of iterations taken with dual infeasibility. iinfitem.sim dual iter Number of dual simplex iterations during the last optimization. iinfitem.sim network dual deg iter The number of dual network degenerate iterations. iinfitem.sim network dual hotstart If 1 then the dual network simplex algorithm is solving from an advanced basis. iinfitem.sim network dual hotstart lu If 1 then a valid basis factorization of full rank was located and used by the dual network simplex algorithm. iinfitem.sim network dual inf iter The number of iterations taken with dual infeasibility in the network optimizer. iinfitem.sim network dual iter Number of dual network simplex iterations during the last optimization. iinfitem.sim network primal deg iter The number of primal network degenerate iterations. iinfitem.sim network primal hotstart If 1 then the primal network simplex algorithm is solving from an advanced basis. iinfitem.sim network primal hotstart lu If 1 then a valid basis factorization of full rank was located and used by the primal network simplex algorithm. iinfitem.sim network primal inf iter The number of iterations taken with primal infeasibility in the network optimizer. iinfitem.sim network primal iter Number of primal network simplex iterations during the last optimization. iinfitem.sim numcon Number of constraints in the problem solved by the simplex optimizer. iinfitem.sim numvar Number of variables in the problem solved by the simplex optimizer. iinfitem.sim primal deg iter The number of primal degenerate iterations.
D.13. INTEGER INFORMATION ITEMS. 567 iinfitem.sim primal dual deg iter The number of degenerate major iterations taken by the primal dual simplex algorithm. iinfitem.sim primal dual hotstart If 1 then the primal dual simplex algorithm is solving from an advanced basis. iinfitem.sim primal dual hotstart lu If 1 then a valid basis factorization of full rank was located and used by the primal dual simplex algorithm. iinfitem.sim primal dual inf iter The number of master iterations with dual infeasibility taken by the primal dual simplex algorithm. iinfitem.sim primal dual iter Number of primal dual simplex iterations during the last optimization. iinfitem.sim primal hotstart If 1 then the primal simplex algorithm is solving from an advanced basis. iinfitem.sim primal hotstart lu If 1 then a valid basis factorization of full rank was located and used by the primal simplex algorithm. iinfitem.sim primal inf iter The number of iterations taken with primal infeasibility. iinfitem.sim primal iter Number of primal simplex iterations during the last optimization. iinfitem.sim solve dual Is non-zero if dual problem is solved. iinfitem.sol bas prosta Problem status of the basic solution. Updated after each optimization. iinfitem.sol bas solsta Solution status of the basic solution. Updated after each optimization. iinfitem.sol int prosta Deprecated. iinfitem.sol int solsta Degrecated. iinfitem.sol itg prosta Problem status of the integer solution. Updated after each optimization.
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The MOSEK Python optimizer API manu
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Contents 1 Changes and new features
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CONTENTS v 8 A case study 97 8.1 Po
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CONTENTS vii 14.2.1 Caveats . . . .
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CONTENTS ix A.2.70 Task.getdviolvar
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CONTENTS xi A.2.162Task.isdouparnam
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CONTENTS xiii A.2.254Task.readparam
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CONTENTS xv B.1.45 dparam.mio near
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CONTENTS xvii B.2.68 iparam.log sim
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CONTENTS xix B.2.160iparam.sim refa
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CONTENTS xxi D.18 Long integer info
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Contact information Phone +45 3917
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License agreement Before using the
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Chapter 1 Changes and new features
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1.4. API CHANGES 7 1.3.3 Mixed-inte
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1.10. SUMMARY OF API CHANGES 9 •
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Chapter 2 About this manual This ma
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Chapter 3 Getting support and help
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Chapter 4 Testing installation and
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Chapter 5 Basic API tutorial In thi
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5.1. THE BASICS 23 5 # 6 # Purpose:
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5.2. LINEAR OPTIMIZATION 25 and the
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5.2. LINEAR OPTIMIZATION 27 Load a
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5.2. LINEAR OPTIMIZATION 29 Optimiz
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5.2. LINEAR OPTIMIZATION 31 88 task
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5.2. LINEAR OPTIMIZATION 33 51 mose
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5.3. CONIC QUADRATIC OPTIMIZATION 3
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5.3. CONIC QUADRATIC OPTIMIZATION 3
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5.4. SEMIDEFINITE OPTIMIZATION 39 m
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5.4. SEMIDEFINITE OPTIMIZATION 41 4
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5.4. SEMIDEFINITE OPTIMIZATION 43 1
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5.5. QUADRATIC OPTIMIZATION 45 with
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5.5. QUADRATIC OPTIMIZATION 47 68 #
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5.5. QUADRATIC OPTIMIZATION 49 5.5.
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5.5. QUADRATIC OPTIMIZATION 51 81 a
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5.6. THE SOLUTION SUMMARY 53 • Th
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5.7. INTEGER OPTIMIZATION 55 21 # f
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5.7. INTEGER OPTIMIZATION 57 137 sy
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5.8. THE SOLUTION SUMMARY FOR MIXED
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5.9. RESPONSE HANDLING 61 Note that
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5.10. PROBLEM MODIFICATION AND REOP
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5.10. PROBLEM MODIFICATION AND REOP
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5.11. SOLUTION ANALYSIS 67 151 # Pu
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5.13. CONVENTIONS EMPLOYED IN THE A
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Chapter 6 Nonlinear API tutorial Th
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6.1. SEPARABLE CONVEX (SCOPT) INTER
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Chapter 7 Advanced API tutorial Thi
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7.1. THE PROGRESS CALL-BACK 87 71 p
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7.2. SOLVING LINEAR SYSTEMS INVOLVI
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Chapter 8 A case study 8.1 Portfoli
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8.1. PORTFOLIO OPTIMIZATION 99 e T
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8.1. PORTFOLIO OPTIMIZATION 101 is
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8.1. PORTFOLIO OPTIMIZATION 103 15
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8.1. PORTFOLIO OPTIMIZATION 107 8.1
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8.1. PORTFOLIO OPTIMIZATION 111 z j
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8.1. PORTFOLIO OPTIMIZATION 113 Var
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Chapter 9 Usage guidelines The purp
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9.3. WRITING TASK DATA TO A FILE 12
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Chapter 10 Problem formulation and
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10.1. LINEAR OPTIMIZATION 125 be a
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10.2. CONIC QUADRATIC OPTIMIZATION
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10.2. CONIC QUADRATIC OPTIMIZATION
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10.3. SEMIDEFINITE OPTIMIZATION 131
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10.4. QUADRATIC AND QUADRATICALLY C
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Chapter 11 The optimizers for conti
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11.1. HOW AN OPTIMIZER WORKS 137 11
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11.2. LINEAR OPTIMIZATION 139 11.2.
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11.2. LINEAR OPTIMIZATION 141 Whene
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11.2. LINEAR OPTIMIZATION 143 11.2.
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11.2. LINEAR OPTIMIZATION 145 • R
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11.5. NONLINEAR CONVEX OPTIMIZATION
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11.6. SOLVING PROBLEMS IN PARALLEL
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Chapter 12 The optimizers for mixed
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12.3. THE MIXED-INTEGER CONIC OPTIM
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12.5. TERMINATION CRITERION 159 •
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12.7. UNDERSTANDING SOLUTION QUALIT
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Chapter 13 The analyzers 13.1 The p
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13.1. THE PROBLEM ANALYZER 165 Cons
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13.2. ANALYZING INFEASIBLE PROBLEMS
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Chapter 14 Primal feasibility repai
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14.2. AUTOMATIC REPAIR 179 One way
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14.3. FEASIBILITY REPAIR IN MOSEK 1
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14.3. FEASIBILITY REPAIR IN MOSEK 1
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Chapter 15 Sensitivity analysis 15.
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15.4. SENSITIVITY ANALYSIS FOR LINE
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15.5. SENSITIVITY ANALYSIS FROM THE
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15.6. SENSITIVITY ANALYSIS WITH THE
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15.6. SENSITIVITY ANALYSIS WITH THE
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Appendix A API reference This chapt
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201 • Task.relaxprimal Obtain inf
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A.1. EXCEPTIONS 203 • Task.putvar
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A.2. CLASS TASK 205 Arguments which
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A.2. CLASS TASK 207 See also • Ro
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A.2. CLASS TASK 209 Description: Ap
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A.2. CLASS TASK 211 Description: If
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A.2. CLASS TASK 213 A.2.16 Task.com
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A.2. CLASS TASK 215 subj : int[] In
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A.2. CLASS TASK 217 firsti : int In
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A.2. CLASS TASK 219 A.2.27 Task.get
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A.2. CLASS TASK 221 valijkl : Descr
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A.2. CLASS TASK 225 idx : long Inde
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A.2. CLASS TASK 229 i : int Index o
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A.2. CLASS TASK 233 conetype : cone
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A.2. CLASS TASK 235 Description: Ob
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A.2. CLASS TASK 237 sub : int[] Ind
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A.2. CLASS TASK 241 Computes the vi
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A.2. CLASS TASK 253 k : int Index o
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A.2. CLASS TASK 255 A.2.103 Task.ge
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A.2. CLASS TASK 257 normalize : int
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A.2. CLASS TASK 259 Description: Le
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A.2. CLASS TASK 261 max(l x j τ
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A.2. CLASS TASK 265 last : int Last
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A.2. CLASS TASK 269 Arguments snx :
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A.2. CLASS TASK 271 slc : double[]
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A.2. CLASS TASK 273 Arguments accmo
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A.2. CLASS TASK 277 last : int Valu
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A.2. CLASS TASK 279 subi : int[] Ro
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A.2. CLASS TASK 283 Arguments taskn
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A.2. CLASS TASK 287 vartype : varia
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A.2. CLASS TASK 289 whichsol : solt
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A.2. CLASS TASK 293 A.2.162 Task.is
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A.2. CLASS TASK 295 whichstream : s
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A.2. CLASS TASK 297 A.2.170 Task.pr
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A.2. CLASS TASK 299 markj : mark Th
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A.2. CLASS TASK 301 Prints a part o
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A.2. CLASS TASK 303 A.2.175 Task.pu
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A.2. CLASS TASK 305 j : int Index o
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A.2. CLASS TASK 309 Description: Th
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A.2. CLASS TASK 313 Changes the bou
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A.2. CLASS TASK 315 Modifies one li
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A.2. CLASS TASK 317 bkc : boundkey
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A.2. CLASS TASK 319 Arguments j : i
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A.2. CLASS TASK 321 parvalue : int
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A.2. CLASS TASK 325 Sets a string p
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A.2. CLASS TASK 327 Description: Re
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A.2. CLASS TASK 329 qosubj : int[]
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A.2. CLASS TASK 331 Sets the status
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A.2. CLASS TASK 335 Arguments sux :
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A.2. CLASS TASK 337 y : double[] Ve
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A.2. CLASS TASK 341 last : int Last
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A.2. CLASS TASK 343 See also • Ta
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A.2. CLASS TASK 347 Description: Se
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A.2. CLASS TASK 349 Sets a slice of
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A.2. CLASS TASK 351 A.2.254 Task.re
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A.2. CLASS TASK 353 wux : double[]
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A.2. CLASS TASK 355 See also • Ta
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A.2. CLASS TASK 357 isdef : int Is
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A.2. CLASS TASK 359 A.2.269 Task.st
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A.2. CLASS TASK 361 Description: Wr
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A.3. CLASS ENV 363 A.2.278 Task.wri
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A.3. CLASS ENV 365 Arguments code :
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A.3. CLASS ENV 367 Arguments keepdl
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A.4. CALLBACK FUNCTIONS AND RELATED
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A.6. ALL FUNCTIONS BY NAME 371 Task
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Appendix B Parameters Parameters gr
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391 • iparam.log file. If turned
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393 • dparam.intpnt nl tol rel st
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395 • iparam.mio construct sol. C
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397 • dparam.intpnt nl tol mu red
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399 • iparam.mio mode. Turns on/o
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401 • iparam.sim solve form. Cont
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B.1. DPARAM: DOUBLE PARAMETERS 403
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B.2. IPARAM: INTEGER PARAMETERS 427
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B.3. SPARAM: STRING PARAMETER TYPES
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Page 537 and 538: Appendix C Response codes Response
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Page 541 and 542: 519 rescode.err global inv conic pr
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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 and 560: 537 rescode.err user nlo func The u
Page 561 and 562: 539 rescode.wrn ana almost int boun
Page 563 and 564: 541 rescode.wrn mio infeasible fina
Page 565 and 566: 543 rescode.wrn write discarded cfi
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Page 569 and 570: D.5. PROGRESS CALL-BACK CODES 547 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: D.13. INTEGER INFORMATION ITEMS. 56
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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
Page 611 and 612: F.1. THE MPS FILE FORMAT 589 Fields
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Page 615 and 616: F.1. THE MPS FILE FORMAT 593 Constr
Page 617 and 618: F.1. THE MPS FILE FORMAT 595 v 1 is
Page 619 and 620: F.1. THE MPS FILE FORMAT 597 Please
Page 621 and 622: F.2. THE LP FILE FORMAT 599 minimiz
Page 623 and 624: F.2. THE LP FILE FORMAT 601 st defi
Page 625 and 626: F.2. THE LP FILE FORMAT 603 bounds
Page 627 and 628: F.3. THE OPF FORMAT 605 iparam.writ
Page 629 and 630: F.3. THE OPF FORMAT 607 [tag "value
Page 631 and 632: F.3. THE OPF FORMAT 609 Note that a
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Page 635 and 636: F.3. THE OPF FORMAT 613 [bounds] [b
Page 637 and 638: F.4. THE TASK FORMAT 615 This can b
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F.7. THE SOLUTION FILE FORMAT 617 c
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Appendix G Problem analyzer example
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G.2. ARKI001 621 2 476 45.42 48.19
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G.4. PROBLEM WITH BOTH LINEAR AND C
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Bibliography [1] Chvátal, V.. Line
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Index analyzenames (Task method), 2
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INDEX 629 getpcni (Task method), 25
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INDEX 631 putbarablocktriplet (Task
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INDEX 633 rescode.err inv numj, 521
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INDEX 635 rescode.err sen bound inv
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