The MOSEK command line tool Version 7.0 (Revision 141)

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320 CHAPTER 18. PROBLEM ANALYZER EXAMPLES distrib: A i rows rows% acc% 1 3600 14.20 14.20 2 10803 42.60 56.79 [3, 7] 3995 15.75 72.55 8 6962 27.45 100.00 Column nonzeros, A|j range: min A|j: 0 (0%) max A|j: 61 (0.240536%) distrib: A|j cols cols% acc% 0 3602 11.12 11.12 1 10800 33.33 44.45 2 7200 22.22 66.67 [3, 7] 7279 22.46 89.13 [8, 15] 3521 10.87 100.00 [32, 61] 1 0.00 100.00 3600/3602 empty columns correspond to variables used in conic and/or quadratic constraints only A nonzeros, A(ij) range: min |A(ij)|: 0.00833333 max |A(ij)|: 1.00000 distrib: A(ij) coeffs [0.00833, 0.01) 57280 [0.01, 0.1) 59 [0.1, 1] 36000 ------------------------------------------------------------------------------- Constraint bounds, lb
Bibliography [1] R. Fourer and D. M. Gay and B. W. Kernighan. AMPL. A modeling language for mathematical programming, 2nd edition, 2003. Thomson [2] MOSEK ApS. MOSEK Modeling manual, 2012. Last revised January 31 2013. http://docs.mosek.com/generic/modeling-a4.pdf [3] Andersen, E. D. and Andersen, K. D.. Presolving in linear programming. Math. Programming 2:221-245 [4] Andersen, E. D., Gondzio, J., Mészáros, Cs. and Xu, X.. Implementation of interior point methods for large scale linear programming, Interior-point methods of mathematical programming p. 189-252, 1996. Kluwer Academic Publishers [5] Erling D. Andersen. The homogeneous and self-dual model and algorithm for linear optimization. Technical report TR-1-2009, 2009. MOSEK ApS. http://www.mosek.com/fileadmin/reports/tech/homolo.pdf [6] Andersen, E. D. and Ye, Y.. Combining interior-point and pivoting algorithms. Management Sci. December 12:1719-1731 [7] Ahuja, R. K., Magnanti, T. L. and Orlin, J. B.. Network flows, Optimization, vol. 1 p. 211-369, 1989. North Holland, Amsterdam [8] Andersen, E. D., Roos, C. and Terlaky, T.. On implementing a primal-dual interior-point method for conic quadratic optimization. Math. Programming February 2 [9] Andersen, E. D. and Ye, Y.. A computational study of the homogeneous algorithm for large-scale convex optimization. Computational Optimization and Applications 10:243- 269 [10] Andersen, E. D. and Ye, Y.. On a homogeneous algorithm for the monotone complementarity problem. Math. Programming February 2:375-399 [11] Wolsey, L. A.. Integer programming, 1998. John Wiley and Sons [12] Chvátal, V.. Linear programming, 1983. W.H. Freeman and Company [13] Roos, C., Terlaky, T. and Vial, J. -Ph.. Theory and algorithms for linear optimization: an interior point approach, 1997. John Wiley and Sons, New York 321
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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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Chapter 10 Response codes Response
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205 MSK RES ERR CON Q NOT PSD The q
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207 MSK RES ERR GLOBAL INV CONIC PR
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209 MSK RES ERR INV MARKJ Invalid v
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211 MSK RES ERR INVALID FORMAT TYPE
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213 MSK RES ERR LICENSE NO SERVER S
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215 MSK RES ERR MIO NO OPTIMIZER No
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217 MSK RES ERR NAME MAX LEN A name
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219 MSK RES ERR NUMCONLIM Maximum n
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221 MSK RES ERR QCON UPPER TRIANGLE
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223 MSK RES ERR SYM MAT INVALID COL
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225 MSK RES ERR USER NLO FUNC The u
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227 MSK RES WRN ANA ALMOST INT BOUN
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229 MSK RES WRN MIO INFEASIBLE FINA
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231 MSK RES WRN WRITE DISCARDED CFI
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Chapter 11 API constants 11.1 Const
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11.5. PROGRESS CALL-BACK CODES 235
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11.6. TYPES OF CONVEXITY CHECKS. 24
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11.10. DOUBLE INFORMATION ITEMS 245
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11.10. DOUBLE INFORMATION ITEMS 247
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11.11. FEASIBILITY REPAIR TYPES 249
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11.13. INTEGER INFORMATION ITEMS. 2
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11.17. LANGUAGE SELECTION CONSTANTS
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11.22. MIXED-INTEGER NODE SELECTION
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11.29. ORDERING STRATEGIES 261 MSK
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11.34. PROBLEM STATUS KEYS 263 MSK
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11.38. SENSITIVITY TYPES 265 MSK SC
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11.44. SOLUTION ITEMS 267 MSK SIM S
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Page 291 and 292: 12.3. THE PARAMETER FILE 275 -min F
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Page 307 and 308: 14.1. THE SECTIONS 291 x1 * x2 Ther
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Page 319 and 320: 15.4. WRITING OPF FILES FROM MOSEK
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Page 327 and 328: Chapter 17 The solution file format
Page 329 and 330: 17.2. THE INTEGER SOLUTION FILE 313
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