## Big-M and convex hulls

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

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

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

A little known solver

Mixed-integer representations of nonlinear operators

Epi- and hypograph conic representations of nonlinear operators

Callback representations of nonlinear operators

Working with nonlinear operators in a structured and efficient fashion

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

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

…or both?

Wanted but not needed

Crap in crap out

= ≠ ==. Horse and sheep purchases and warehouse logistics

How bad is exponential complexity?

Extremely common

…but I won’t do that.

Where to start?

Where to start?

Asking for the impossible

Where why how?

Code works for almost all cases, but suddenly fails.

All solver and YALMIP to crash for diagnostics

Be careful with unnecessary symbolic overhead

Uncertainty descriptions can only involve uncertain variables, so how can they be parameterized?

How do I create a cheap Ferrari?

Give your solver a hint

Extracting inputs and outputs from solvers

Slice’n dice your problems

Extract dual solutions from conic optimization problems.

Complex data in optimization models. No problem in reality.

Name your constraints for easy reference

Avoid that for-loop by using vector objectives

sqrt, sqrtm, power or cpower?