Documentation

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

CVXPY Pyomo GAMS AMPL
YALMIP JuMP CVX PuLP
Mathematica Frontline solvers Drake AmpliconArchitect

A more comprehensive list of supported third-party interfaces is provided here.

Additional technical documentation

Modeling

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
Piecewise linear approximation of a convex function CQO Python, Fusion approximation, regression, least squares, convex fitting, piecewise linear
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

MOSEK Modeling Cookbook

Our Modeling Cookbook is a guide to the theory and practice of conic optimization. Learn how to express your optimization problem in conic form.