The MOSEK command line tool Version 7.0 (Revision 141)

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254 CHAPTER 11. API CONSTANTS MSK IINF SIM NETWORK DUAL ITER Number of dual network simplex iterations during the last optimization. MSK IINF SIM NETWORK PRIMAL DEG ITER The number of primal network degenerate iterations. MSK IINF SIM NETWORK PRIMAL HOTSTART If 1 then the primal network simplex algorithm is solving from an advanced basis. MSK IINF 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. MSK IINF SIM NETWORK PRIMAL INF ITER The number of iterations taken with primal infeasibility in the network optimizer. MSK IINF SIM NETWORK PRIMAL ITER Number of primal network simplex iterations during the last optimization. MSK IINF SIM NUMCON Number of constraints in the problem solved by the simplex optimizer. MSK IINF SIM NUMVAR Number of variables in the problem solved by the simplex optimizer. MSK IINF SIM PRIMAL DEG ITER The number of primal degenerate iterations. MSK IINF SIM PRIMAL DUAL DEG ITER The number of degenerate major iterations taken by the primal dual simplex algorithm. MSK IINF SIM PRIMAL DUAL HOTSTART If 1 then the primal dual simplex algorithm is solving from an advanced basis. MSK IINF 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. MSK IINF SIM PRIMAL DUAL INF ITER The number of master iterations with dual infeasibility taken by the primal dual simplex algorithm. MSK IINF SIM PRIMAL DUAL ITER Number of primal dual simplex iterations during the last optimization. MSK IINF SIM PRIMAL HOTSTART If 1 then the primal simplex algorithm is solving from an advanced basis.
11.13. INTEGER INFORMATION ITEMS. 255 MSK IINF SIM PRIMAL HOTSTART LU If 1 then a valid basis factorization of full rank was located and used by the primal simplex algorithm. MSK IINF SIM PRIMAL INF ITER The number of iterations taken with primal infeasibility. MSK IINF SIM PRIMAL ITER Number of primal simplex iterations during the last optimization. MSK IINF SIM SOLVE DUAL Is non-zero if dual problem is solved. MSK IINF SOL BAS PROSTA Problem status of the basic solution. Updated after each optimization. MSK IINF SOL BAS SOLSTA Solution status of the basic solution. Updated after each optimization. MSK IINF SOL INT PROSTA Deprecated. MSK IINF SOL INT SOLSTA Degrecated. MSK IINF SOL ITG PROSTA Problem status of the integer solution. Updated after each optimization. MSK IINF SOL ITG SOLSTA Solution status of the integer solution. Updated after each optimization. MSK IINF SOL ITR PROSTA Problem status of the interior-point solution. Updated after each optimization. MSK IINF SOL ITR SOLSTA Solution status of the interior-point solution. Updated after each optimization. MSK IINF STO NUM A CACHE FLUSHES Number of times the cache of A elements is flushed. A large number implies that maxnumanz is too small as well as an inefficient usage of MOSEK. MSK IINF STO NUM A REALLOC Number of times the storage for storing A has been changed. A large value may indicates that memory fragmentation may occur. MSK IINF STO NUM A TRANSPOSES Number of times the A matrix is transposed. A large number implies that maxnumanz is too small or an inefficient usage of MOSEK. This will occur in particular if the code alternate between accessing rows and columns of A.
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The MOSEK command line tool. Versio
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Contents 1 Changes and new features
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CONTENTS v 6.5 Termination criterio
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CONTENTS vii 9.1.56 MSK DPAR NONCON
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CONTENTS ix 9.2.79 MSK IPAR MIO FEA
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CONTENTS xi 9.2.171 MSK IPAR SOL RE
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CONTENTS xiii 11.29 Ordering strate
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CONTENTS xv 18.2 arki001 . . . . .
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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. OPTIMIZATION TOOLBOX FOR MATLA
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Chapter 2 What is MOSEK MOSEK is a
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Chapter 3 MOSEK and AMPL AMPL is a
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3.6. CONSTRAINT AND VARIABLE NAMES
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3.8. HOT-START 15 Linear dependency
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3.10. SENSITIVITY ANALYSIS 17 • .
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Chapter 4 Problem formulation and s
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4.1. LINEAR OPTIMIZATION 21 be a pr
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4.2. CONIC QUADRATIC OPTIMIZATION 2
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4.2. CONIC QUADRATIC OPTIMIZATION 2
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4.3. SEMIDEFINITE OPTIMIZATION 27 4
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4.5. GENERAL CONVEX OPTIMIZATION 29
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4.5. GENERAL CONVEX OPTIMIZATION 31
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Chapter 5 The optimizers for contin
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5.1. HOW AN OPTIMIZER WORKS 35 5.1.
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5.2. LINEAR OPTIMIZATION 37 5.2.2 T
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5.2. LINEAR OPTIMIZATION 39 Wheneve
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5.2. LINEAR OPTIMIZATION 41 5.2.2.3
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5.2. LINEAR OPTIMIZATION 43 • Rai
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5.5. NONLINEAR CONVEX OPTIMIZATION
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5.6. SOLVING PROBLEMS IN PARALLEL 4
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Chapter 6 The optimizers for mixed-
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6.3. THE MIXED-INTEGER CONIC OPTIMI
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6.5. TERMINATION CRITERION 53 The f
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6.7. UNDERSTANDING SOLUTION QUALITY
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Chapter 7 The analyzers 7.1 The pro
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7.1. THE PROBLEM ANALYZER 59 Constr
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7.2. ANALYZING INFEASIBLE PROBLEMS
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Chapter 8 Sensitivity analysis 8.1
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8.4. SENSITIVITY ANALYSIS FOR LINEA
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8.5. SENSITIVITY ANALYSIS WITH THE
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8.5. SENSITIVITY ANALYSIS WITH THE
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Chapter 9 Parameters Parameters gro
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81 • MSK SPAR ITR SOL FILE NAME.
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83 • MSK DPAR INTPNT NL TOL REL G
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85 • MSK IPAR LOG MIO. Controls t
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87 • MSK DPAR INTPNT NL MERIT BAL
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89 • MSK IPAR INFEAS PREFER PRIMA
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91 • MSK IPAR SIM SAVE LU. Contro
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9.1. MSKDPARAME: DOUBLE PARAMETERS
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9.2. MSKIPARAME: INTEGER PARAMETERS
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9.3. MSKSPARAME: STRING PARAMETER T
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Page 219 and 220: Chapter 10 Response codes Response
Page 221 and 222: 205 MSK RES ERR CON Q NOT PSD The q
Page 223 and 224: 207 MSK RES ERR GLOBAL INV CONIC PR
Page 225 and 226: 209 MSK RES ERR INV MARKJ Invalid v
Page 227 and 228: 211 MSK RES ERR INVALID FORMAT TYPE
Page 229 and 230: 213 MSK RES ERR LICENSE NO SERVER S
Page 231 and 232: 215 MSK RES ERR MIO NO OPTIMIZER No
Page 233 and 234: 217 MSK RES ERR NAME MAX LEN A name
Page 235 and 236: 219 MSK RES ERR NUMCONLIM Maximum n
Page 237 and 238: 221 MSK RES ERR QCON UPPER TRIANGLE
Page 239 and 240: 223 MSK RES ERR SYM MAT INVALID COL
Page 241 and 242: 225 MSK RES ERR USER NLO FUNC The u
Page 243 and 244: 227 MSK RES WRN ANA ALMOST INT BOUN
Page 245 and 246: 229 MSK RES WRN MIO INFEASIBLE FINA
Page 247 and 248: 231 MSK RES WRN WRITE DISCARDED CFI
Page 249 and 250: Chapter 11 API constants 11.1 Const
Page 251 and 252: 11.5. PROGRESS CALL-BACK CODES 235
Page 259 and 260: 11.6. TYPES OF CONVEXITY CHECKS. 24
Page 261 and 262: 11.10. DOUBLE INFORMATION ITEMS 245
Page 263 and 264: 11.10. DOUBLE INFORMATION ITEMS 247
Page 265 and 266: 11.11. FEASIBILITY REPAIR TYPES 249
Page 267 and 268: 11.13. INTEGER INFORMATION ITEMS. 2
Page 269: 11.13. INTEGER INFORMATION ITEMS. 2
Page 273 and 274: 11.17. LANGUAGE SELECTION CONSTANTS
Page 275 and 276: 11.22. MIXED-INTEGER NODE SELECTION
Page 277 and 278: 11.29. ORDERING STRATEGIES 261 MSK
Page 279 and 280: 11.34. PROBLEM STATUS KEYS 263 MSK
Page 281 and 282: 11.38. SENSITIVITY TYPES 265 MSK SC
Page 283 and 284: 11.44. SOLUTION ITEMS 267 MSK SIM S
Page 285 and 286: 11.46. SOLUTION TYPES 269 11.46 Sol
Page 287 and 288: 11.52. INTEGER VALUES 271 11.52 Int
Page 289 and 290: Chapter 12 MOSEK Command line tool
Page 291 and 292: 12.3. THE PARAMETER FILE 275 -min F
Page 293 and 294: Chapter 13 The MPS file format MOSE
Page 295 and 296: 13.1. MPS FILE STRUCTURE 279 Extens
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Page 299 and 300: 13.1. MPS FILE STRUCTURE 283 Field
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Page 303 and 304: 13.2. INTEGER VARIABLES 287 13.2 In
Page 305 and 306: Chapter 14 The LP file format MOSEK
Page 307 and 308: 14.1. THE SECTIONS 291 x1 * x2 Ther
Page 309 and 310: 14.2. LP FORMAT PECULIARITIES 293 1
Page 311 and 312: 14.3. THE STRICT LP FORMAT 295 MSK
Page 313 and 314: Chapter 15 The OPF format The Optim
Page 315 and 316: 15.2. THE FILE FORMAT 299 [con ’c
Page 317 and 318: 15.2. THE FILE FORMAT 301 - ‘NEAR
Page 319 and 320: 15.4. WRITING OPF FILES FROM MOSEK
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15.5. EXAMPLES 305 [/hints] [variab
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15.5. EXAMPLES 307 x1 x2 [/integer]
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Chapter 16 The XML (OSiL) format MO
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Chapter 17 The solution file format
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17.2. THE INTEGER SOLUTION FILE 313
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Chapter 18 Problem analyzer example
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18.2. ARKI001 317 2 476 45.42 48.19
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18.4. PROBLEM WITH BOTH LINEAR AND
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Bibliography [1] R. Fourer and D. M
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Index AMPL outlev, 13 wantsol, 13 a
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INDEX 325 MSK RES ERR FEASREPAIR IN
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INDEX 327 MSK RES ERR MPS NULL VAR
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INDEX 329 MSK RES TRM MAX TIME, 226
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