LMIs in Control/Click here to continue/YALMIP

YALMIP The tool mainly uses for this book. The chapter will provide some examples for using Yalmip with optimization problem. YAMLIP's download link: . click "download" in the top and follows the instructions in the file. YALMIP can solve some easy problems without extra software support. For a optimize problem, we need to define constraints, objective function, variables. For example, in order to find a straight line to separate the two data in the plot with minimal implementation error.% It's good practice to start by clearing YALMIPs internal database % Every time you call sdpvar etc, an internal database grows larger yalmip('clear') % Define variables x = sdpvar(10,1); % Define constraints Constraints = [sum(x) <= 10, x(1) == 0, 0.5 <= x(2) <= 1.5]; for i = 1 : 7 Constraints = [Constraints, x(i) + x(i+1) <= x(i+2) + x(i+3)]; end % Define an objective Objective = x'*x+norm(x,1); % Set some options for YALMIP and solver options = sdpsettings('verbose',1,'solver','quadprog','quadprog.maxiter',100); % Solve the problem sol = optimize(Constraints,Objective,options); % Analyze error flags if sol.problem == 0 % Extract and display value solution = value(x) else display('Hmm, something went wrong!'); sol.info yalmiperror(sol.problem) end