What Is Optimization Toolbox?

6 Argument and Options ReferenceOutput Arguments (Continued)Argument Description Used by FunctionsfvalgradhessianjacobianlambdaThe value of the objective functionfun at the solution x.Thevalueofthegradientoffunat the solution x. If fun does notcompute the gradient, grad is afinite-differencing approximationof the gradient.ThevalueoftheHessianoffunat the solution x. For large-scalemethods, if fun does not computethe Hessian, hessian is afinite-differencing approximationof the Hessian. For medium-scalemethods, hessian is the value ofthe Quasi-Newton approximationto the Hessian at the solution x.The value of the Jacobian of funat the solution x. If fun does notcompute the Jacobian, jacobian isa finite-differencing approximationof the Jacobian.TheLagrangemultipliersatthesolution x. lambda is a structurewhere each field is for a differentconstraint type. For structure fieldnames, see individual functiondescriptions. (For lsqnonneg,lambda is simply a vector, aslsqnonneg only handles one kindof constraint.)fgoalattain, fminbnd, fmincon,fminimax, fminsearch, fminunc,fseminf, fsolve, fzero, linprog,quadprogfmincon, fminuncfmincon, fminuncmaxfval max{fun(x)} at the solution x. fminimaxlsqcurvefit, lsqnonlin, fsolvefgoalattain, fmincon, fminimax,fseminf, linprog, lsqcurvefit,lsqlin, lsqnonlin, lsqnonneg,quadprog6-6