![]() Thank you Torabi and especially rosewc for help. Option 1: fmincon (ObjFun,x0,A,b,Aeq,beq,lb,ub,nonlcon,options) or Option 2: fmincon (ObjFun,x0,A,b,Aeq,beq,lb,ub,nonlcon,options) in which nonlcon (used to add the nonlinear constraints to the model) can be a function name or a function handle (by putting before function's name). ![]() Setting the Initial conditions x0.x1 = 0.01 Looking at the problem again to check showproblem(prob1) X5 = optimvar("x5","LowerBound",0,"UpperBound",1) Ĭreating an objective function edit functionĬreating optimizing expression obj = Objective function prob1.Objective = obj This evaluation is wasteful when you use the same calculation. X2 = optimvar("x2","LowerBound",0,"UpperBound",1) Solvers such as fmincon evaluate the objective and nonlinear constraint functions separately. This is how I did itĬreating an empty problem prob1 = optimproblemÄefining Optimization variables with bounds x1 = optimvar("x1", "LowerBound",0) It's a very good approach for anyone else as well. I went down the road suggested by Kalvelagen. nonlinear equations that are defined or declared in the environment. % Minimize function frbegin() subject to constraints. Constrained Optimization using Matlabs fmincon. %FRBEGIN Function specified by rbeginner on stack overflow, X1=x(1) x2=x(2) x3=x(3) x4=x(4) x5=x(5) Ĭ= Īnd the function to minimize is just function z = frbegin(x) % Convert each constraint to equation that is <=0 when constraint is met. X(1) > 0 %handle this constraint by set lb(1)=0 % These are the constarints on function frbegin0(). %FRBEGINCON Constraints specified by rbeginner on stack overflow, I tried calling fmincon() with the function below for nonlcon, and had good results, for many random starting points. There are no equality constraints, so the value of 0 is returned for ceq. The first constraint, x(1)>0, is implemented by setting the lower bound for x(1) to 0, and function frbeginCon() takes care of the other constraints. It has an optional argument nonlcon, which is the name of a function that returns 2 arrays, c and ceq: the values of equations for the inequality constraints (if any), and the values of the equations for the equality constraints (if any). % Function frbeginner2() is to be minimized subject to constraints Here's what not to do: function Z = frbeginner2(x) It doesn't work well, probably because the discontinuities in the function cause problems for the built-in routine fmincon(). ![]() Each constraint is inverted, and 10^6 is added to the function return value if the inverse of a constraint is true. This is not elegant, but it would be easy. However, you can always rewrite the polygon constraint, with x the vector of optimization variables as Axif a point is in a polygon is a yes/no decision. You could modify your function so that a large value is added each time a constraint is violated. 1 Answer Sorted by: 0 No, because that would introduce non-smoothness in your optimization problem.
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