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

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6 CHAPTER 1. CHANGES AND NEW FEATURES IN MOSEK Platform OS version C compiler linux32x86 Redhat 5 or newer (LSB 3+) Intel C 13.1 (gcc 4.3, glibc 2.3.4) linux64x86 RedHat 5 or newer (LSB 3+) Intel C 13.1 (gcc 4.3, glibc 2.3.4) osx64x86 OSX 10.7 Lion or newer Intel C 13.0 (llvm-gcc-4.2) win32x86 Windows XP, Server 2003 or newer Intel C 13.0 (VS 2008) win64x86 Windows XP, Server 2003 or newer Intel C 13.1 (VS 2008) Interface Supported versions Java Sun Java 1.6+ Microsoft.NET 2.1+ Python 2 2.6+ Python 3 3.1+ Table 1.1: Supported platforms • The MBT file format has been replaced by a new task format. The new format supports semidefinite optimization. • the HTML version of the documentation is no longer included in the downloads to save space. It is still available online. • MOSEK is more restrictive about the allowed names on variables etc. This is in particular the case when writing LP files. • MOSEK no longer tries to detect the cache sizes and is in general less sensitive to the hardware. • The parameter is set iparam.auto update sol info is default off. In previous version it was by default on. • The function relaxprimal has been deprecated and replaced by the function primalrepair. 1.3 Optimizers 1.3.1 Interior point optimizer • The factorization routines employd by the interior-point optimizer for linear and conic optimization problems has been completely rewritten. In particular the dense column detection and handling is improved. The factorization routine will also exploit vendor tuned BLAS routines. 1.3.2 The simplex optimizers • No major changes.
1.4. API CHANGES 7 1.3.3 Mixed-integer optimizer • A new mixed-integer for linear and conic problems has been introduced. It is from run-to-run determinitic and is parallelized. It is particular suitable for conic problems. 1.4 API changes • Added support for semidefinite optimization. • Some clean up has been performed implying some functions have been renamed. 1.5 Optimization toolbox for MATLAB • A MOSEK equivalent of bintprog has been introduced. • The functionality of the MOSEK version of linprog has been improved. It is now possible to employ the simplex optimizer in linprog. • mosekopt now accepts a dense A matrix. • An new method for specification of cones that is more efficient when the problem has many cones has introduced. The old method is still allowed but is deprecated. • Support for semidefinite optimization problems has been added to the toolbox. 1.6 License system • Flexlm has been upgraded to version 11.11. 1.7 Other changes • The documentation has been improved. 1.8 Interfaces • Semi-definite optimization capabilities have been add to the optimizer APIs. • A major clean up have occured in the optimizer APIs. This should have little effect for most users. • A new object orientated interface called Fusion has been added. Fusion is available Java, MAT- LAB, .NET and Python. • The AMPL command line tool has been updated to the latest version.
Page 1 and 2: The MOSEK Python optimizer API manu
Page 3 and 4: Contents 1 Changes and new features
Page 5 and 6: CONTENTS v 8 A case study 97 8.1 Po
Page 7 and 8: CONTENTS vii 14.2.1 Caveats . . . .
Page 9 and 10: CONTENTS ix A.2.70 Task.getdviolvar
Page 11 and 12: CONTENTS xi A.2.162Task.isdouparnam
Page 13 and 14: CONTENTS xiii A.2.254Task.readparam
Page 15 and 16: CONTENTS xv B.1.45 dparam.mio near
Page 17 and 18: CONTENTS xvii B.2.68 iparam.log sim
Page 19 and 20: CONTENTS xix B.2.160iparam.sim refa
Page 21 and 22: CONTENTS xxi D.18 Long integer info
Page 23 and 24: Contact information Phone +45 3917
Page 25 and 26: License agreement Before using the
Page 27: Chapter 1 Changes and new features
Page 31 and 32: 1.10. SUMMARY OF API CHANGES 9 •
Page 37 and 38: Chapter 2 About this manual This ma
Page 39 and 40: Chapter 3 Getting support and help
Page 41 and 42: Chapter 4 Testing installation and
Page 43 and 44: Chapter 5 Basic API tutorial In thi
Page 45 and 46: 5.1. THE BASICS 23 5 # 6 # Purpose:
Page 47 and 48: 5.2. LINEAR OPTIMIZATION 25 and the
Page 49 and 50: 5.2. LINEAR OPTIMIZATION 27 Load a
Page 51 and 52: 5.2. LINEAR OPTIMIZATION 29 Optimiz
Page 53 and 54: 5.2. LINEAR OPTIMIZATION 31 88 task
Page 55 and 56: 5.2. LINEAR OPTIMIZATION 33 51 mose
Page 57 and 58: 5.3. CONIC QUADRATIC OPTIMIZATION 3
Page 59 and 60: 5.3. CONIC QUADRATIC OPTIMIZATION 3
Page 61 and 62: 5.4. SEMIDEFINITE OPTIMIZATION 39 m
Page 63 and 64: 5.4. SEMIDEFINITE OPTIMIZATION 41 4
Page 65 and 66: 5.4. SEMIDEFINITE OPTIMIZATION 43 1
Page 67 and 68: 5.5. QUADRATIC OPTIMIZATION 45 with
Page 69 and 70: 5.5. QUADRATIC OPTIMIZATION 47 68 #
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Page 75 and 76: 5.6. THE SOLUTION SUMMARY 53 • Th
Page 77 and 78: 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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Appendix C Response codes Response
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517 rescode.err con q not psd The q
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519 rescode.err global inv conic pr
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521 rescode.err inv markj Invalid v
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523 rescode.err invalid format type
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525 rescode.err license no server s
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527 rescode.err mio no optimizer No
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529 rescode.err name max len A name
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531 rescode.err numconlim Maximum n
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533 rescode.err qcon upper triangle
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535 rescode.err sym mat invalid col
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537 rescode.err user nlo func The u
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539 rescode.wrn ana almost int boun
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541 rescode.wrn mio infeasible fina
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543 rescode.wrn write discarded cfi
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Appendix D API constants D.1 Constr
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D.5. PROGRESS CALL-BACK CODES 547 c
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D.6. TYPES OF CONVEXITY CHECKS. 555
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D.10. DOUBLE INFORMATION ITEMS 557
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D.10. DOUBLE INFORMATION ITEMS 559
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D.11. FEASIBILITY REPAIR TYPES 561
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D.13. INTEGER INFORMATION ITEMS. 56
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D.16. INPUT/OUTPUT MODES 569 intpnt
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D.20. CONTINUOUS MIXED-INTEGER SOLU
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D.26. OBJECTIVE SENSE TYPES 573 D.2
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D.31. PRESOLVE METHOD. 575 paramete
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D.35. RESPONSE CODE TYPE 577 D.35 R
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D.42. PROBLEM REFORMULATION. 579 si
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D.46. SOLUTION TYPES 581 solsta.dua
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D.50. STREAM TYPES 583 startpointty
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Appendix E Troubleshooting When cre
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Appendix F Mosek file formats MOSEK
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F.1. THE MPS FILE FORMAT 589 Fields
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F.1. THE MPS FILE FORMAT 591 must b
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F.1. THE MPS FILE FORMAT 593 Constr
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F.1. THE MPS FILE FORMAT 595 v 1 is
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F.1. THE MPS FILE FORMAT 597 Please
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F.2. THE LP FILE FORMAT 599 minimiz
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F.2. THE LP FILE FORMAT 601 st defi
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F.2. THE LP FILE FORMAT 603 bounds
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F.3. THE OPF FORMAT 605 iparam.writ
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F.3. THE OPF FORMAT 607 [tag "value
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F.3. THE OPF FORMAT 609 Note that a
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F.3. THE OPF FORMAT 611 F.3.2.3 Nam
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F.3. THE OPF FORMAT 613 [bounds] [b
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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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