MOSEK Optimizer API for Python 10.2.2

This manual describes the features of the Optimizer API, the matrix-oriented optimization interface of MOSEK.

Contents:

• 1 Introduction
  • 1.1 Why the Optimizer API for Python?
• 2 Contact Information
• 3 License Agreement
  • 3.1 MOSEK end-user license agreement
  • 3.2 Third party licenses
• 4 Installation
  • 4.1 PIP and Wheels
  • 4.2 Anaconda
  • 4.3 Manual installation
  • 4.4 Testing the Installation
• 5 Design Overview
  • 5.1 Modeling
  • 5.2 “Hello World!” in MOSEK
• 6 Optimization Tutorials
  • 6.1 Linear Optimization
  • 6.2 From Linear to Conic Optimization
  • 6.3 Conic Quadratic Optimization
  • 6.4 Power Cone Optimization
  • 6.5 Conic Exponential Optimization
  • 6.6 Geometric Programming
  • 6.7 Semidefinite Optimization
  • 6.8 Integer Optimization
  • 6.9 Disjunctive constraints
  • 6.10 Quadratic Optimization
  • 6.11 Problem Modification and Reoptimization
  • 6.12 Parallel optimization
  • 6.13 Retrieving infeasibility certificates
• 7 Solver Interaction Tutorials
  • 7.1 Environment and task
  • 7.2 Accessing the solution
  • 7.3 Errors and exceptions
  • 7.4 Input/Output
  • 7.5 Setting solver parameters
  • 7.6 Retrieving information items
  • 7.7 Progress and data callback
  • 7.8 MOSEK OptServer
• 8 Debugging Tutorials
  • 8.1 Understanding optimizer log
  • 8.2 Addressing numerical issues
  • 8.3 Debugging infeasibility
  • 8.4 Python Console
• 9 Advanced Numerical Tutorials
  • 9.1 Solving Linear Systems Involving the Basis Matrix
  • 9.2 Calling BLAS/LAPACK Routines from MOSEK
  • 9.3 Computing a Sparse Cholesky Factorization
• 10 Technical guidelines
  • 10.1 Memory management and garbage collection
  • 10.2 Names
  • 10.3 Multithreading
  • 10.4 Efficiency
  • 10.5 The license system
  • 10.6 Deployment
• 11 Case Studies
  • 11.1 Portfolio Optimization
  • 11.2 Logistic regression
  • 11.3 Concurrent optimizer
• 12 Problem Formulation and Solutions
  • 12.1 Linear Optimization
  • 12.2 Conic Optimization
  • 12.3 Semidefinite Optimization
  • 12.4 Quadratic and Quadratically Constrained Optimization
• 13 Optimizers
  • 13.1 Presolve
  • 13.2 Linear Optimization
  • 13.3 Conic Optimization - Interior-point optimizer
  • 13.4 The Optimizer for Mixed-Integer Problems
• 14 Additional features
  • 14.1 Problem Analyzer
  • 14.2 Automatic Repair of Infeasible Problems
  • 14.3 Sensitivity Analysis
• 15 API Reference
  • 15.1 API Conventions
  • 15.2 Functions grouped by topic
  • 15.3 Class Env
  • 15.4 Class Task
  • 15.5 Exceptions
  • 15.6 Parameters grouped by topic
  • 15.7 Parameters (alphabetical list sorted by type)
  • 15.8 Response codes
  • 15.9 Enumerations
  • 15.10 Function Types
  • 15.11 Supported domains
• 16 Supported File Formats
  • 16.1 The LP File Format
  • 16.2 The MPS File Format
  • 16.3 The OPF Format
  • 16.4 The CBF Format
  • 16.5 The PTF Format
  • 16.6 The Task Format
  • 16.7 The JSON Format
  • 16.8 The Solution File Format
• 17 List of examples
• 18 Interface changes
  • 18.1 Important changes compared to version 9
  • 18.2 Changes compared to version 9
• 19 Bibliography