ObjectiveLimit and the iterate is feasible, then the If the objective function value goes below Least ObjectiveImprovementThreshold lower: (fold – fnew)/(1 + |fold|) > Solution only when it locates another with an objective function value that is at 'mininfeas' - Explore the node with the minimal 'minobj' - Explore the node with the minimum Maximum amount of time in seconds allowed for the Strictly positive integer that is the maximum number of nodes the solverĮxplores in its branch-and-bound process. MaxFeasiblePoints integer feasible points. Maximum number of function evaluations allowed.įgoalattain, fminbnd, fmincon, fminimax, fminsearch, fminunc, fseminf, fsolve, lsqcurvefit, lsqnonlin LPOptimalityTolerance for a variable to be taken into the Nonnegative real where reduced costs must exceed Per node during the branch-and-bound process. Strictly positive integer, the maximum number of simplex algorithm iterations Ĭoneprog, lsqlin 'interior-point' algorithm and quadprog 'interior-point-convex' algorithm 'schur' - Schur complement method step solver. 'prodchol' - Product form Cholesky step solver. 'augmented' - Augmented form step solver. If the problem is sparse, the step solver is For optimset, use HessMultĪlgorithm for searching for feasible points (see Heuristics for Finding Feasible Solutions): User-supplied Hessian multiply function, specified as a function User-supplied Hessian, specified as a function handle (see Including Hessians). Termination tolerance on the function value.įgoalattain, fmincon, fminimax, fminsearch, fminunc, fseminf, fsolve, lsqcurvefit, lsqlin, lsqnonlin, quadprog Their evaluation in fmincon interior-point evaluations if 'central' differences might violate bounds during (centered), which takes twice as many function evaluations but should be moreĪccurate. For optimset, use FinDiffRelStepįinite differences, used to estimate gradients, are either Is sqrt(eps) for forward finite differences, and eps^(1/3) You set FiniteDifferenceStepSize to a vector v, theĭelta = v.*max(abs(x),TypicalX) Scalar FiniteDifferenceStepSize expands to a vector. Scalar or vector step size factor for finite differences. Necessary, to have fgoalattain achieve the first Specify the number of objectives required for the objectiveįun to equal the set goal. See the individual function reference pages for the values thatĬhooses the algorithm for achieving feasibility in theĭifferent algorithm than the default false. 'final-detailed' displays just the final output, andĪll. 'final' displays just the final output, and gives the 'notify-detailed' displays output only if theįunction does not converge, and gives the technical exit message. Not converge, and gives the default exit message. 'notify' displays output only if the function does 'iter-detailed' displays output at each iteration, 'iter' displays output at each iteration, and gives Level of cut generation (see Cut Generation): For optimset, use DerivativeCheckĬoneprog, fgoalattain, fmincon, fminimax, fseminf, intlinprog, linprog, lsqlin, quadprog 'maxfun' - The fractional component with maximalĬorresponding component in the absolute value of objective vectorĬompare user-supplied analytic derivatives (gradients or Jacobian,ĭepending on the selected solver) to finite differencingįgoalattain, fmincon, fminimax, fminunc, fseminf, fsolve, lsqcurvefit, lsqnonlin Maximum pseudocost, with an even more careful estimate of pseudocost than in 'reliability' - The fractional component with Maximum pseudocost, with a careful estimate of pseudocost. 'strongpscost' - The fractional component with 'maxpscost' - The fractional component with
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