Experiment No. 01 Name of the Experiment: fminunc-Unconstrained Nonlinear and Linear programming Minimization in MATLAB fminunc-Unconstrained Nonlinear Minimization: Equation: 5*x(1)^2+2*x(2)^2+4*x(1)*x(2)+3*x(2)+1 function f = objfunc(x) f=(5*x(1)^2+2*x(2)^2+4*x(1)*x(2)+3*x(2)+1); options=optimoptions(@fminunc,'Algorithm','quasi-newton'); x0=[-1,1]; [x,fval,exitflag,output]=fminunc(@objfunc,x0,options); Local minimum found. Optimization completed because the size of the gradient is less than the default value of the optimality tolerance. <stopping criteria details> >> output output = iterations: 5 funcCount: 18 stepsize: 1.6241e-05 lssteplength: 1 firstorderopt: 5.9605e-08 algorithm: 'quasi-newton' message: 'Local minimum found.…' >> x x= 0.5000 -1.2500 1 >> fval fval = -0.8750 >> exitflag exitflag = 1 Another Way: Optimization Tool: Figure 1.1: Solving Using Optimization Tool 2 Figure 1.2: Current Step Linear Programming: Equation: 𝑀𝑎𝑥, 𝑧 = 4𝑥1 + 5𝑥2 + 𝑥3 𝑥1 + 3𝑥2 + 𝑥3 ≤ 50 3𝑥1 + 2𝑥2 + 4𝑥3 ≤ 60 3𝑥1 + 4𝑥2 ≤ 20 3 f = [-4 -5 -1] f= -4 -5 -1 >> A=[1 3 1;3 2 4;3 4 0] A= 1 3 1 3 2 4 3 4 0 >> b=[50;60;20] b= 50 60 20 >> Aeq=[] Aeq = [] >> beq=[] beq = [] >> lb=[0 0 0] lb = 0 0 0 >> ub=[] ub = [] >> [x z]=linprog(f,A,b,Aeq,beq,lb,ub) Optimization terminated. x= 0.0000 4 5.0000 12.5000 z= -37.5000 >> z=z*-1 z= 37.5000 5