Provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc...
|Author||Sebastien Bihorel, Michael Baudin (author of the original module)|
|Date of publication||2014-03-02 08:47:40|
|Maintainer||Sebastien Bihorel <firstname.lastname@example.org>|
asserts: Check of Variable Class
ones: Matrix of zeros or ones.
optimbase: S3 optimbase classes
optimbase.checkbounds: Check bounds.
optimbase.checkcostfun: Check Cost Function
optimbase.checkshape: Check the Dimensions of the Cost Function Output
optimbase.checkx0: Check Consistency of Initial Guesses
optimbase.destroy: Erase an optimization history.
optimbase.function: Call Cost Function
optimbase.get: Get the value for the given element
optimbase.gridsearch: Grid evaluation of a constrained or unconstrained cost...
optimbase.hasbounds: Query for Bounds and Constraints
optimbase.incriter: Iteration Log Incrementation
optimbase.isfeasible: Check Point Estimate
optimbase.isinbounds: Point Estimate Comparison with Bounds and Constraints
optimbase.log: Optimbase Log functions
optimbase.outputcmd: Call user-defined output function
optimbase.outstruct: Create Basic Optimization Data Object
optimbase-package: R port of the Scilab optimbase module
optimbase.proj2bnds: Projection of Point Estimate to Bounds
optimbase.set: Optimization Object Configuration
optimbase.terminate: Evaluation of Termation Status
size: Vector, Matrix or Data.Frame Size
strvec: Auto-collapse of Vectors
transpose: Vector and Matrix Transpose
vec2matrix: Vector to Matrix Conversion