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The MOSEK Python API manual.
Version 5.0 (Revision 127).

Contact information
License agreement
1. Changes and new features in MOSEK
- 1.1. File formats
- 1.2. Optimizers
- 1.3. API changes
- 1.4. License system
- 1.5. Other changes
- 1.6. Interfaces
- 1.7. Supported platforms
2. About this manual
3. Getting support and help
- 3.1. MOSEK documentation
- 3.2. Additional reading
4. Testing installation and Running examples
- 4.1. Microsoft Windows Platform
  - 4.1.1. Running an example
- 4.2. Linux Platform
  - 4.2.1. Executing a Python program
5. Basic API tutorial
- 5.1. The basics
- 5.2. Linear optimization
  - 5.2.1. Example: lo1
    - 5.2.1.1. Source code
    - 5.2.1.2. Example code comments
  - 5.2.2. An alternative implementation: lo2
- 5.3. Quadratic optimization
  - 5.3.1. Example: Quadratic objective
    - 5.3.1.1. Source code
    - 5.3.1.2. Example code comments
  - 5.3.2. Example: Quadratic constraints
    - 5.3.2.1. Source code
- 5.4. Conic optimization
  - 5.4.1. Example: cqo1
    - 5.4.1.1. Source code
    - 5.4.1.2. Source code comments
- 5.5. Integer optimization
- 5.6. Problem modification and reoptimization
- 5.7. Efficiency considerations
  - 5.7.1. API overhead
- 5.8. Conventions employed in the API
6. Modelling
- 6.1. Linear optimization
  - 6.1.1. Duality for linear optimization
  - 6.1.2. Primal and dual infeasible case
- 6.2. Linear network flow problems
- 6.3. Quadratic and quadratically constrained optimization
  - 6.3.1. A general recommendation
  - 6.3.2. Reformulating as a separable quadratic problem
- 6.4. Conic optimization
- 6.5. Recommendations
  - 6.5.1. Avoid nearly infeasible models
- 6.6. Examples continued
  - 6.6.1. The absolute value
  - 6.6.2. The Markowitz portfolio model
7. The optimizers for continuous problems
- 7.1. How an optimizer works
- 7.2. Linear optimization
- 7.3. Linear network optimization
  - 7.3.1. Network flow problems
  - 7.3.2. Embedded network problems
- 7.4. Conic optimization
  - 7.4.1. The interior-point optimizer
    - 7.4.1.1. Interior-point termination criteria
- 7.5. Nonlinear convex optimization
  - 7.5.1. The interior-point optimizer
    - 7.5.1.1. Interior-point termination criteria
- 7.6. Solving problems in parallel
8. The optimizer for mixed integer problems
- 8.1. Some notation
- 8.2. An important fact about integer optimization problems
- 8.3. How the integer optimizer works
- 8.4. Termination criterion
- 8.5. How to speed up the solution process
9. Analyzing infeasible problems
- 9.1. Example: Primal infeasibility
- 9.2. Theory concerning infeasible problems
  - 9.2.1. Certificat of primal infeasibility
  - 9.2.2. Certificat of dual infeasibility
10. Primal feasibility repair
- 10.1. The main idea
- 10.2. Feasibility repair in MOSEK
11. Sensitivity analysis
- 11.1. Introduction
- 11.2. Restrictions
- 11.3. References
- 11.4. Sensitivity analysis for linear problems
- 11.5. Sensitivity analysis from the MOSEK API
- 11.6. Sensitivity analysis with the command line tool
12. API developer guidelines
- 12.1. Turn on logging
- 12.2. Turn on data checking
- 12.3. Debugging an optimization task
- 12.4. Error handling
- 12.5. Check the problem status and solution status
- 12.6. Important API limitations
  - 12.6.1. Thread safety
- 12.7. Bug reporting
13. API reference
- 13.1. API Functionality
- 13.2. Class mosek.ArrayLengthException
- 13.3. Class mosek.Env
- 13.4. Class mosek.Error
  - 13.4.1. Constructors
- 13.5. Class mosek.Exception
  - 13.5.1. Constructors
- 13.6. Class mosek.Task
- 13.7. Class mosek.Warning
  - 13.7.1. Constructors
14. Parameter reference
- 14.1. Parameter groups
- 14.2. Double parameters
- 14.3. Integer parameters
- 14.4. String parameter types
15. Response codes
16. Constants
- 16.1. Constraint or variable access modes
- 16.2. Basis identification
- 16.3. Bound keys
- 16.4. Specifies the branching direction.
- 16.5. Progress call-back codes
- 16.6. Types of convexity checks.
- 16.7. Compression types
- 16.8. Cone types
- 16.9. CPU type
- 16.10. Data format types
- 16.11. Double information items
- 16.12. Double values
- 16.13. Feasibility repair types
- 16.14. Integer information items.
- 16.15. Information item types
- 16.16. Input/output modes
- 16.17. Bound keys
- 16.18. Continuous mixed integer solution type
- 16.19. Integer restrictions
- 16.20. Mixed integer node selection types
- 16.21. MPS file format type
- 16.22. Message keys
- 16.23. Network detection method
- 16.24. Objective sense types
- 16.25. On/off
- 16.26. Optimizer types
- 16.27. Ordering strategies
- 16.28. Parameter type
- 16.29. Presolve method.
- 16.30. Problem data items
- 16.31. Problem types
- 16.32. Problem status keys
- 16.33. Interpretation of quadratic terms in MPS files
- 16.34. Response code type
- 16.35. Scaling type
- 16.36. Sensitivity types
- 16.37. Degeneracy strategies
- 16.38. Hot-start type employed by the simplex optimizer
- 16.39. Simplex selection strategy
- 16.40. Solution items
- 16.41. Solution status keys
- 16.42. Solution types
- 16.43. Solve primal or dual form
- 16.44. Status keys
- 16.45. Starting point types
- 16.46. Stream types
- 16.47. Integer values
- 16.48. Variable types
- 16.49. XML writer output mode
A. The MPS file format
- A.1. The MPS file format
- A.2. Integer variables
- A.3. General limitations
- A.4. Interpretation of the MPS format
- A.5. The free MPS format
B. The LP file format
- B.1. A warning
- B.2. The LP file format
C. The OPF format
- C.1. Intended use
- C.2. The file format
- C.3. Parameters section
- C.4. Writing OPF files from MOSEK
- C.5. Examples
D. The XML (OSiL) format
E. The ORD file format
- E.1. An example
F. The solution file format
- F.1. The basic and interior solution files
- F.2. The integer solution file
Bibliography
Index

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Wed May 13 06:31:54 2009