nlpsolve computes the optimum of a (not necessarily differentiable) nonlinear (multivariate) objective function, subject to a set of nonlinear equality and/or inequality constraints, using the COBYLA algorithm. The command takes the following arguments:
nlpsolve returns a list containing the optimal value of the objective and a vector of optimal values of the decision variables.
The objective is minimized by default, unless maximize or maximize=true is specified as an option.
Initial point, if given, does not need to be feasible. Note, however, that the initial value of a variable must not be zero. If the initial point is not given or isn’t feasible, a feasible starting guess is automatically generated. Note that choosing a good initial point is needed for obtaining a correct solution in some cases.
Input syntax for nlpsolve resembles that of Maple’s NLPSolve (entering the objective as a function (univariate case) is not supported, however).
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