Tools to Support Optimization Possibly with Bounds and Masks
A reorganization of the call to numDeriv grad() function.
Forward difference numerical gradient approximation.
Perform axial search around a supposed minimum and provide diagnostics
Check bounds and masks for parameter constraints used in nonlinear opt...
Compute the maximum step along a search direction.
set control defaults
Run tests, where possible, on user objective function
Generate gradient and Hessian for a function at given parameters.
Generate gradient and Hessian for a function at given parameters.
Backward difference numerical gradient approximation.
Central difference numerical gradient approximation.
Run tests, where possible, on user objective function and (optionally)...
Run tests, where possible, on user objective function and (optionally)...
Check Kuhn Karush Tucker conditions for a supposed function minimum
Tools to Support Optimization Possibly with Bounds and Masks
Check the scale of the initial parameters and bounds input to an optim...
Tools to assist in safely applying user generated objective and derivative function to optimization programs. These are primarily function minimization methods with at most bounds and masks on the parameters. Provides a way to check the basic computation of objective functions that the user provides, along with proposed gradient and Hessian functions, as well as to wrap such functions to avoid failures when inadmissible parameters are provided. Check bounds and masks. Check scaling or optimality conditions. Perform an axial search to seek lower points on the objective function surface. Includes forward, central and backward gradient approximation codes.