## Installation

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If it’s hard, you’re doing it wrong.

## Getting started

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

## Linear programming

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

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

## Second order cone programming

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Ice-cream cone! Yummy.

## Semidefinite programming

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

## Determinant maximization

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

## Duality

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Extract dual solutions from conic optimization problems.

## Sum-of-squares programming

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

## Robust optimization

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

## Rank constrained semidefinite programming problems

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Learn how to constrain ranks in semidefinite programs

## Nonlinear operators - integer models

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Mixed-integer representations of nonlinear operators

## Nonlinear operators - graphs and conic models

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Epi- and hypograph conic representations of nonlinear operators

## Nonlinear operators - callbacks

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Callback representations of nonlinear operators

## Nonlinear operators

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Working with nonlinear operators in a structured and efficient fashion

## Multiparametric programming

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This tutorial requires MPT.

## Moment relaxations

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

## Logics and integer-programming representations

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

## Integer programming

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

## Global optimization

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

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## General convex programming

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

## Exponential cone programming

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Convex conic optimization over exponentials and logarithms

## Envelope approximations for global optimization

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

## Complex-valued problems

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Complex data in optimization models. No problem in reality.

## Bilevel programming

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Bilevel programming using the built-in bilevel solver

## Big-M and convex hulls

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

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