Documentation and API reference (MOSEK version 10.0)
| General | Introduction | Installation | Licensing | Release notes | FAQ |
| Fusion API | C++ | Java | .NET | Python | |
| Optimizer API | C | Java | .NET | Python | |
| Other interfaces | Toolbox (MATLAB) | Rmosek (R) | Command line | OptServer |
Documentation and API reference (MOSEK version 9.3)
| General | Introduction | Installation | Licensing | Release notes | FAQ |
| Fusion API | C++ | Java | .NET | Python | |
| Optimizer API | C | Java | .NET | Python | |
| Other interfaces | Toolbox (MATLAB) | Rmosek (R) | Command line | OptServer |
Documentation for older versions is included with the distribution.
Third-party interfaces to MOSEK
A more comprehensive list of supported third-party interfaces is provided here.
Additional technical documentation
- FlexNet License Administration Guide (PDF) - the detailed license system guide for advanced users. For most users the MOSEK licensing guide above should be sufficient.
Modeling
- The MOSEK Modeling Cookbook (HTML) - is a mathematically oriented publication about conic optimization which presents the theory, examples and many tips and tricks about formulating optimization problems. Also available as PDF (A4) and PDF (letter).
- Portfolio Optimization with MOSEK - a collection of portfolio optimization models, code samples and the MOSEK Portfolio Optimization Cookbook.
- Conic Modeling Cheatsheet (PDF).
The MOSEK Notebook Collection
A collection of tutorials which demonstrate how to model and solve various optimization problems with MOSEK. Further case studies can be found in the documentation and on MOSEK GitHub.
| Title | Type | Tools | Keywords |
|---|---|---|---|
| Introduction to Fusion | Python, Fusion | ||
| Least squares regression | CQO | Python, Fusion | regression, LSE, regularization, lasso, ridge, Huber penalty |
| Small online example | CQO | Python, Fusion | OptServer, regression, online |
| Linear regression techniques | CQO, POW | Python, Fusion | regression, 2-norm, 1-norm, deadzone, p-norm, Czebyshew |
| Stochastic risk measures | LO, EXP, POW | Python, Fusion | portfolio, risk measures, value-at-risk, VaR, CVaR |
| Risk parity portfolio selection | EXP | Python, Fusion | risk parity, portfolio |
| Unit commitment | MICQO | Python, Fusion | unit commitment, production planning |
| SINR Optimization | GP, EXP | Python, Fusion | geometric program, log-sum-exp, signal-to-noise, interference |
| Filter design | SDO | Python, Fusion | trigonometric polynomials, Czebyshev lowpass filter |
| Binary quadratic problems | QP, SDP | Python, Fusion | SDP relaxation, branch and bound, binary QP |
| Subcarrier and power allocation | MIO, EXP | Python, Fusion | power allocation, data rate, channel allocation, F-SPARC |
| Geometric facility location | MICQO | Python, Fusion | planar coverage, wireless network design |
| Smallest enclosing sphere | CQO | Python, Fusion | geometry, dualization |
| Optimization of cycles on surfaces | LO | Python, Fusion | geometry, topology, triangulation |
| Equilibrium of masses with springs | CQO | Python, Fusion | mechanical equilibrium, potential energy |
| Exact planar cover | MIO | Python | combinatorial, binary variables, certificate |
| Approximating uncertain inequalities | EXP | Python, Fusion | adjustable robust, approximation, safe region |
| Wasserstein barycenter | LO | Fusion, CVXPY, Pyomo | Wasserstein distance, averaging, barycenter |
| Wasserstein barycenter with regularization | EXP | Fusion, CVXPY | Wasserstein distance, entropy, barycenter, regularization |
| Wasserstein barycenter (Julia) | LO | Julia, JuMP | Wasserstein distance, averaging, barycenter |
| Wasserstein barycenter with regularization (Julia) | EXP | Julia, JuMP | Wasserstein distance, entropy, barycenter, regularization |
| Utility based option pricing | CQO, EXP, POW | Python, Fusion | stochastic process, reservation price, portfolio, option pricing, HARA utility |
| Distributionally robust portfolio | LO | Python, Fusion | stochastic optimization, robust optimization, portfolio, Wasserstein metric |
Publications and Technical Reports
| Title | Assoc. files | Date | Rev. date |
|---|---|---|---|
| Symmetry detection in Mixed-Integer Conic Programming | 5-sep-2022 | ||
| Projection onto the exponential cone: a univariate root-finding problem | 12-jan-2021 | ||
| A computational practicability study of MIQCQP reformulations | 08-dec-2020 | 10-dec-2020 | |
| A primal-dual interior-point algorithm for nonsymmetric exponential-cone optimization | 27-may-2019 | 10-mar-2021 | |
| On formulating quadratic functions in optimization models | 04-dec-2014 | 02-mar-2016 | |
| How to use Farkas' lemma to say something important about infeasible linear problems | 12-sep-2011 | ||
| The homogeneous and self-dual model and algorithm for linear optimization | 21-oct-2013 | ||
| MOSEK related publications | 19-aug-2011 |
