The options allows the user to set various parameters of the Optimization problem. The routine calls Ipopt for solving the Constrained Optimization problem, Ipopt is a library written in C++. Search the minimum of a constrained optimization problem specified by : hessian :Ī matrix of doubles, containing the Lagrangian's hessian of the solution. gradient :Ī vector of doubles, containing the Objective's gradient of the solution. lambda :Ī structure, containing the Lagrange multipliers of lower bound, upper bound and constraints at the optimized point. output :Ī structure, containing the information about the optimization. xopt :Ī vector of doubles, cointating the computed solution of the optimization problem fopt :Ī scalar of double, containing the the function value at x exitflag :Ī scalar of integer, containing the flag which denotes the reason for termination of algorithm. options :Ī list, containing the option for user to specify. Refer Example for definition of Constraint function. It is declared in such a way that non-linear inequality constraints are defined first as a single row vector (c), followed by non-linear equality constraints as another single row vector (ceq). A function, representing the objective function of the problem x0 :Ī vector of doubles, containing the starting values of variables of size (1 X n) or (n X 1) where 'n' is the number of Variables A :Ī matrix of doubles, containing the coefficients of linear inequality constraints of size (m X n) where 'm' is the number of linear inequality constraints b :Ī vector of doubles, related to 'A' and containing the the Right hand side equation of the linear inequality constraints of size (m X 1) Aeq :Ī matrix of doubles, containing the coefficients of linear equality constraints of size (m1 X n) where 'm1' is the number of linear equality constraints beq :Ī vector of doubles, related to 'Aeq' and containing the the Right hand side equation of the linear equality constraints of size (m1 X 1) lb :Ī vector of doubles, containing the lower bounds of the variables of size (1 X n) or (n X 1) where 'n' is the number of Variables ub :Ī vector of doubles, containing the upper bounds of the variables of size (1 X n) or (n X 1) where 'n' is the number of Variables nlc :Ī function, representing the Non-linear Constraints functions(both Equality and Inequality) of the problem. Title( 'Sequence of Points Computed by fmincon') 15 to x(1) to separate label from plotted 'o'. % Label points with iteration number and add title. % Concatenate current search direction with % searchdir. Hold on case 'iter' % Concatenate current point and objective function % value with history. 'Display', 'iter', 'Algorithm', 'active-set') At the end of each fmincon iteration, the output function does the following:įunction = runfmincon This example shows how to use an output function to monitor the fmincon solution process for solving a constrained nonlinear optimization problem. You can determine which solvers can use an output function by looking in the Options section of function reference pages. Generally, the solvers that employ an output function can take nonlinear functions as inputs. For the problem-based approach, see Output Function for Problem-Based Optimization. This example uses the solver-based approach for output functions. See Output Function and Plot Function Syntax for details and syntax. To do so, create an output function that the optimization function calls at each iteration. For example, you might want to find the sequence of points that the algorithm computes and plot those points. For some problems, you might want output from an optimization algorithm at each iteration.
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