Thank you Torabi and especially rosewc for help. Setting the Initial conditions x0.x1 = 0.01 Looking at the problem again to check showproblem(prob1) Setting constraints constr1 = x1 - (x4*x3) - x2 <=0 Ĭonstr3 = (x4*(x3))*(x2-((1-x3)/2))-(x1*(x2-x1+(x4*x3)))<=0 Python equivalent for Matlab's fmincon for constrained nonlinear optimization Ask Question Asked 1 year, 5 months ago Modified 11 months ago Viewed 998 times 1 I'm currently trying to implement a Python script for solving a constrained nonlinear optimization problem with 1000 variables and 700 constraints (both linear and nonlinear). X5 = optimvar("x5","LowerBound",0,"UpperBound",1) Ĭreating an objective function edit functionĬreating optimizing expression obj = Objective function prob1.Objective = obj This is how I did itĬreating an empty problem prob1 = optimproblemĭefining 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. Consult the fmincon function reference page. % Minimize function frbegin() subject to constraints. For a problem with a nonlinear objective function and a nonlinear constraint, generally you use the fmincon solver. %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. Function returning the current values of nonlinear inequality constraints (constrained to < 0 ) in the first output and the. 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. You could modify your function so that a large value is added each time a constraint is violated.
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