The solver uses TypicalX for scaling finite differences The default value is ones(numberofvariables,1). The number of elements in x0, the starting point. The number of elements in TypicalX is equal to See Tolerances and Stopping Criteria.įor optimset, the name is TolX. See Current and Legacy Option Names.Ī positive scalar. Or Jacobian information (when using JacobMult),įor optimset, the name is Jacobian, and the valuesĪre 'on' or 'off'. The solver uses a user-defined Jacobian (defined in fun), The solver approximates the Jacobian using finite differences. See Output Functions for Optimization Toolbox™Īnd Output Function and Plot Function Syntax. 'optimplotfirstorderopt' plots the first-order optimalityĬustom plot functions use the same syntax as output functions. 'optimplotresnorm' plots the norm of the residuals. 'optimplotfval' plots the function value. 'optimplotfunccount' plots the function count. Pass a name, a function handle, or aĬell array of names or function handles.
Select from predefined plots or write your own. Plots various measures of progress while the algorithm executes See Output Function and Plot Function Syntax. Pass a function handle or a cell array of function Specify one or more user-defined functions that an optimizationįunction calls at each iteration. Tolerance (stopping criterion) of 1e-4 times FunctionTolerance and The 'levenberg-marquardt' algorithm uses an optimality Termination tolerance on the first-order optimality (a positive See Tolerances and Stopping Criteria and Iterations and Function Counts.įor optimset, the name is MaxIter.
See Current and Legacy Option Names.Ī positive integer. See Tolerances and Stopping Criteria and Iterations and Function Counts.įor optimset, the name is MaxFunEvals. 'on' displays an error when the functionĪllowed, a positive integer. The default is 1e-6.įor optimset, the name is TolFun. Than a forward, difference to avoid evaluating at a point outsideįor optimset, the name is FinDiffType. So, for example, it could take a backward, rather Twice as many function evaluations, but should be more accurate.Īlgorithm is careful to obey bounds when estimating both types ofįinite differences.
See Current and Legacy Option Names.įinite differences, used to estimate gradients,Īre either 'forward' (default), or 'central' (centered). Is sqrt(eps) for forward finite differences, and eps^(1/3)įor optimset, the name is FinDiffRelStep. Level of display (see Iterative Display):ĭelta = v.*max(abs(x),TypicalX) Scalar FiniteDifferenceStepSize expands to a vector. The default is Inf.įinite-difference gradients (a positive scalar). Choices are 'off' (default)įinite-difference gradients (a positive scalar). See Current and Legacy Option Names.Ībout the function to be minimized or solved. (gradients of objective or constraints) to finite-differencing derivatives.įor optimset, the name is DerivativeCheck and the For more information on choosing the algorithm, see Underdetermined that is, the number of equations (the number of elements ofį returned by fun) must be at least as many as It is only a preference, because certain conditions must be met to use each algorithm.įor the trust-region-reflective algorithm, the nonlinear system of equations cannot be Algorithm option specifies a preference for which algorithm to use.
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