YalMip

https://yalmip.github.io/

YALMIP is free of charge to use and is openly distributed, but note that

1. Copyright owned by Johan Löfberg.

2. YALMIP must be referenced (general reference, robust optimization reference, sum-of-squares reference) when used in a published work (give me some credit for saving your valuable time!)

3. YALMIP, or forks or versions of YALMIP, may not be re-distributed as a part of a commercial product unless agreed upon with the copyright owner (if you make money from YALMIP, let me in first!)

4. YALMIP is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY, without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE (if your satellite crash or you fail your Phd due to a bug in YALMIP, your loss!).

5. Forks or versions of YALMIP must include, and follow, this license in any distribution.

For installation, see the installation tutorial, and get started coding here

And don’t forget, most likely you have to install additional solvers

https://github.com/yalmip/YALMIP/archive/master.zip

Installation

Updated: September 17, 2016

If it’s hard, you’re doing it wrong.

Getting started

Updated: September 17, 2016

Tutorial introduces essentially everything you’ll ever need. The remaining 95% is syntactic sugar.

Linear programming

Updated: September 17, 2016

As easy as it gets. Linear separation with linear norms.

Quadratic programming

Updated: September 17, 2016

Almost as easy as linear programming. Be careful though, symbolics might start to cause overhead.

Second order cone programming

Updated: September 17, 2016

Ice-cream cone! Yummy.

Semidefinite programming

Updated: September 17, 2016

Who wudda thought? Optimization over positive definite symmetric matrices is easy.

Determinant maximization

Updated: September 17, 2016

Optimization with ellipsoids and likelihood functions are typical applications of determinant maximization.

Duality

Updated: September 17, 2016

Extract dual solutions from conic optimization problems.

Sum-of-squares programming

Updated: September 17, 2016

Almost nothing is a sum-of-squares, but let’s hope yours is.

Robust optimization

Updated: September 17, 2016

The only thing we can be sure of is the lack of certainty.

Rank constrained semidefinite programming problems

Updated: September 17, 2016

Learn how to constrain ranks in semidefinite programs

Nonlinear operators - integer models

Updated: September 17, 2016

Mixed-integer representations of nonlinear operators

Nonlinear operators - graphs and conic models

Updated: September 17, 2016

Epi- and hypograph conic representations of nonlinear operators

Nonlinear operators - callbacks

Updated: September 17, 2016

Callback representations of nonlinear operators

Nonlinear operators

Updated: September 17, 2016

Working with nonlinear operators in a structured and efficient fashion

Multiparametric programming

Updated: September 17, 2016

This tutorial requires MPT.

Moment relaxations

Updated: September 17, 2016

Moment relaxations allows us to find lower bounds on polynomial optimization problems using semidefinite programming

Logic programming

Updated: September 17, 2016

Logic programming in YALMIP means programming with operators such as alldifferent, number of non-zeros, implications and similiar combinatorial objects.

Integer programming

Updated: September 17, 2016

Undisciplined programming often leads to integer models, but in some cases you have no option.

Global optimization

Updated: September 17, 2016

The holy grail! 60% of the time it works every time.

Geometric programming

Updated: September 17, 2016

Geometric programming. Not about geometry.

General convex programming

Updated: September 17, 2016

YALMIP does not care, but for your own good, think about convexity also in general nonlinear programs.

Exponential cone programming

Updated: September 17, 2016

Convex conic optimization over exponentials and logarithms

Envelope approximations for global optimization

Updated: September 17, 2016

Outer approximations of function envelopes are the core of the global solver BMIBNB

Complex-valued problems

Updated: September 17, 2016

Complex data in optimization models. No problem in reality.

Bilevel programming

Updated: September 17, 2016

Bilevel programming using the built-in bilevel solver

Big-M and convex hulls

Updated: September 17, 2016

Learn how nonconvex models are written as integer programs using big-M strategies, and why it should be called small-M.

Automatic dualization

Updated: September 17, 2016

Primal or dual arbitrary in primal-dual solver? No, but YALMIP can help you reformulate your model.

New release R20160930

Updated: September 30, 2016

Both patches and new features

Sample-based robust optimization

Updated: September 28, 2016

Unintended consequences of an improved optimizer framework

Extensions on the optimizer

Updated: September 28, 2016

Slice’n dice your problems

MATLAB 2016 + CPLEX crash

Updated: September 23, 2016

Boom!

New release R20160923

Updated: September 23, 2016

It’s been a while…

Debugging infeasible models

Updated: September 22, 2016

Where to start?

Octave support in YALMIP

Updated: April 16, 2014

MATLAB no longer required! Recommended though.

Worst-case norms of matrices

Updated: February 08, 2014

Hard? Let’s try anyway.

YALMIP and Simulink

Updated: June 27, 2013

Using YALMIP objects and code in Simulink models, easy or fast, your choice.

Unit commitment example - logic and integer programming

Updated: January 30, 2013

A common application of integer programming is the unit commitment problem in power generation, i.e., scheduling of set of power plants in order to meet a cu...

Nonconvex quadratic programming

Updated: August 31, 2011

Common question: how can I solve a nonconvex QP using SeDuMi? Weird question, but interesting answer.

Strictly feasible sum-of-squares solutions

Updated: February 09, 2011

A question on the YALMIP forum essentially boiled down to how can I generate sum-of-squares solutions which really are feasible, i.e. true certificates?

Work-shop material

Updated: June 11, 2010

Files and exercise material from the YALMIP work-shop at the Swedish control conference 2010

Polytopic geometry using YALMIP and MPT

Updated: June 11, 2010

Ever wondered how to compute the L1 Chebyshev ball?

Tagging constraints

Updated: August 29, 2009

Name your constraints for easy reference

NaN in model

Updated: August 29, 2009

Where why how?

Computing multiple solutions in one shot

Updated: August 29, 2009

Avoid that for-loop by using vector objectives

Constraints without any variables

Updated: August 29, 2009

Code works for almost all cases, but suddenly fails.

New sum-of-squares example

Updated: August 29, 2009

Added a sum-of-squares example focusing on pre- and post-processing capabilities.