set control defaults
Run tests, where possible, on user objective function
Central difference numerical gradient approximation.
A reorganization of the call to numDeriv grad() function.
Backward difference numerical gradient approximation.
Forward difference numerical gradient approximation.
Generate gradient and Hessian for a function at given parameters.
Summarize opm object
Generate gradient and Hessian for a function at given parameters.
Run tests, where possible, on user objective function and (optionally) gradient and hessian
General-purpose optimization
A reorganization of the call to numDeriv grad() function.
General-purpose optimization - multiple starts
Run tests, where possible, on user objective function and (optionally) gradient and hessian
Check Kuhn Karush Tucker conditions for a supposed function minimum
A replacement and extension of the optim() function, plus various
optimization tools
General-purpose optimization
General-purpose optimization
Compact R Implementation of Hooke and Jeeves Pattern Search Optimization
General-purpose optimization
General-purpose optimization - sequential application of methods
Truncated Newton function minimization with bounds constraints
Truncated Newton minimization of an unconstrained function.
Compact display of an optimr()
result object
Safeguarded Newton methods for function minimization using R functions.
Check the scale of the initial parameters and bounds input to an optimization code
used in nonlinear optimization
Summarize optimx object
An R implementation of a nonlinear conjugate gradient algorithm with the Dai / Yuan
update and restart. Based on Nash (1979) Algorithm 22 for its main structure.
An R implementation of a bounded nonlinear conjugate gradient algorithm
with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22
for its main structure. CALL THIS VIA Rcgmin
AND DO NOT USE DIRECTLY.
An R implementation of an unconstrained nonlinear conjugate gradient algorithm
with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22
for its main structure. CALL THIS VIA Rcgmin
AND DO NOT USE DIRECTLY.
Compute the maximum step along a search direction.
Variable metric nonlinear function minimization, driver.
Test if requested solver is present
Perform axial search around a supposed MINIMUM and provide diagnostics
Variable metric nonlinear function minimization with bounds constraints
Check bounds and masks for parameter constraints used in nonlinear optimization
Variable metric nonlinear function minimization, unconstrained