![]() exitflag: A value shows whether the optimization routine is terminated successfully.feval: The objective function value of at the optimal point.The size is the number of design variables. xmin: Vector of the minimum point (optimal point).IntroductionUnconstrained MinimizationĜonstrained Minimization Multiobjective OptimizationConclusion More on fminunc – Output P1,P2,…: To pass additional parameters.option: To set some of the optimization parameters.The guess must be a vector of size of number of design variables. fun: Return a function of objective function.IntroductionUnconstrained MinimizationĜonstrained Minimization Multiobjective OptimizationConclusion More on fminunc – Input = fminunc(fun,x0,options,P1,P2,…) If exitflag > 0, then local minimum is found Some other information IntroductionUnconstrained MinimizationĜonstrained Minimization Multiobjective OptimizationConclusion Results xmin = 0.5000 -1.0000 feval = 1.3028e-010 exitflag = 1 output = iterations: 7 funcCount: 40 stepsize: 1 firstorderopt: 8.1998e-004 algorithm: 'medium-scale: Quasi-Newton line search' Minimum point of design variables Objective function value Exitflag tells if the algorithm is converged. IntroductionUnconstrained MinimizationĜonstrained Minimization Multiobjective OptimizationConclusion Step 2 – Invoke Routine Starting with a guess x0 = options = optimset(‘LargeScale’,’off’) = fminunc(‘objfun’,x0,options) Optimization parameters settings Output arguments Input arguments IntroductionUnconstrained MinimizationĜonstrained Minimization Multiobjective OptimizationConclusion Step 1 – Obj. Then, invoke the unconstrained minimization routine.Create an M-file that returns the function value (Objective Function).IntroductionUnconstrained MinimizationĜonstrained Minimization Multiobjective OptimizationConclusion Steps Consider the problem of finding a set of values T that solves.IntroductionUnconstrained MinimizationĜonstrained Minimization Multiobjective OptimizationConclusion Unconstrained Minimization ![]()
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