The examples are meant to be slightly larger implementations than the problems described in the tutorials, with more realistic setups and actual user problems.
Portfolio: The Portfolio example starts with simple quadratic and second-order cone based portfolio problems, but extends the problem with advanced cardinality constraints. The portfolio example is also used to illustrate an application of multiparametric programming and the optimizer command.
Sudoku: The sudoku example shows how to implement your own Sudoku solver in just a couple of lines. Multidimensional arrays are introduced.
Experiment design: The experiment design example showcases mixed-integer optimization and automatic dualization for determinant optimization problems on a real-world system identification problem.
Unit commitment: The unit commitment example implements the classical power generation and allocation problem, complete with a simulation in closed-loop.
Standard MPC: The first MPC example introduces the basic approach to setup and solve standard MPC problems. The main purpose of the example is to describe how easy it is to generate, solve and simulate MPC problems.
Explicit MPC: The third MPC example computes explicit solutions to the standard and hybrid MPC examples, by coding dynamic programming algorithms directly in the YALMIP language.
MAXPLUS control: The MAXPLUS MPC example combines several advanced features such as convexity propagation and robust optimization, to compute MPC controllers for MAXPLUS systems.
Hybrid MPC: The second MPC example illustrates the use of logic operators to define hybrid MPC problems.
Robust MPC: This MPC example implements some robust optimization ideas for MPC.
Explicit LPV-MPC control: The first LPV-MPC example uses an LPV-MPC approach to compute an explicit controller for the chaotic Henon map. The second LPV-MPC example demostrates the computation of explicit MPC controllers for an LPV systems with parameter-varying input matrix.
Decay-rate: The decay-rate example implements a simple bisection algorithm to solve a quasi-convex SDP problem that often arise in control theory.
LPV control: The LPV control example derives gain-scheduling state-feedback laws. Once again, the purpose of the example is to show-case the robust optimization framework in YALMIP, this time with emphasis on uncertain semidefinite programs.
Bilevel optimization: The bilevel example illustrates how bilevel problems can be formulated and solved using three different approaches.
Sum-of-squares: The sum-of-squares introduces the pre- and post-processing capabilities in YALMIPs sum-of-squares module.