Documentation and API reference (MOSEK version 11.2)
| General | Introduction | Installation | Licensing | Release notes | FAQ | |
| Fusion API | C++ | Java | .NET | Python | ||
| Optimizer API | C | Java | .NET | Python | Julia | Rust |
| Other interfaces | MATLAB (New) | R (Rmosek) | Command line | OptServer | MATLAB (Old) |
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 (source) | Python, Fusion | ||
| Least squares regression (source) | CQO | Python, Fusion | regression, LSE, regularization, lasso, ridge, Huber penalty |
| Linear regression techniques (source) | CQO, POW | Python, Fusion | regression, 2-norm, 1-norm, deadzone, p-norm, Czebyshew |
| Rank-one convexification for sparse regression (source) | CQO, DJC | Python, Fusion | regression, sparsification, 0-norm, disjunctive constraints, regularization |
| Hierarchical model (source) | LO, MILO | Python, Fusion | multiobjective, scheduling, parametrization |
| Max Volume Cuboid (source) | POW, GEO | Python, Fusion | geometric mean, volume maximization, ACC, polyhedra |
| MLE convex density function (source) | EXP, POW | Python, Fusion | MLE, density function |
| GP Toolbox (source) | GP, EXP | Python, Fusion | geometric program, modelling extension |
| Transformer Design (source) | GP, EXP | Python, Fusion | geometric program, engineering, design optimization |
| Stochastic risk measures (source) | LO, EXP, POW | Python, Fusion | portfolio, risk measures, value-at-risk, VaR, CVaR |
| Irreducible Infeasible Subset (IIS) (source) | LO, MILO | Python | IIS, infeasibility, certificate, irreducible set, deletion filter |
| Unit commitment (source) | MICQO | Python, Fusion | unit commitment, production planning |
| SINR Optimization (source) | GP, EXP | Python, Fusion | geometric program, log-sum-exp, signal-to-noise, interference |
| Filter design (source) | SDO | Python, Fusion | trigonometric polynomials, Czebyshev lowpass filter |
| K-means and Euclidean Clustering (source) | CQO, MIO, DJC | Python, Fusion | clustering, k-means, disjunctive constraints |
| Binary quadratic problems (source) | QP, SDP | Python, Fusion | SDP relaxation, branch and bound, binary QP |
| Subcarrier and power allocation (source) | MIO, EXP | Python, Fusion | power allocation, data rate, channel allocation, F-SPARC |
| Geometric facility location (source) | MICQO | Python, Fusion | planar coverage, wireless network design |
| Smallest enclosing ellipsoid (source) | CQO | Python, Fusion | geometry, dualization |
| Truss topology design (source) | CQO | Python, Fusion | structural engineering, truss, equilibrium, stiffness |
| Optimization of cycles on surfaces (source) | LO | Python, Fusion | geometry, topology, triangulation |
| Equilibrium of masses with springs (source) | CQO | Python, Fusion | mechanical equilibrium, potential energy |
| Exact planar cover (source) | MIO | Python | combinatorial, binary variables, certificate |
| Approximating uncertain inequalities (source) | EXP | Python, Fusion | adjustable robust, approximation, safe region |
| Wasserstein barycenter (source) | LO | Fusion, CVXPY, Pyomo | Wasserstein distance, averaging, barycenter |
| Wasserstein barycenter with regularization (source) | EXP | Fusion, CVXPY | Wasserstein distance, entropy, barycenter, regularization |
| Wasserstein barycenter (Julia) (source) | LO | Julia, JuMP | Wasserstein distance, averaging, barycenter |
| Wasserstein barycenter with regularization (Julia) (source) | EXP | Julia, JuMP | Wasserstein distance, entropy, barycenter, regularization |
| Utility based option pricing (source) | CQO, EXP, POW | Python, Fusion | stochastic process, reservation price, portfolio, option pricing, HARA utility |
| Piecewise linear approximation of a convex function (source) | CQO | Python, Fusion | approximation, regression, least squares, convex fitting, piecewise linear |
| Distributionally robust portfolio (source) | 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 | 23-jul-2024 | |
| 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 | 06-mar-2012 | 15-nov-2023 | |
| 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 |
