Optimization
Optimization is intended for the optimization of functions. It has many features,
including a wide selection of descent algorithms, step-length methods, and
"on-the-fly" algorithm switching. Default selections permit you to
use Optimization with a minimum of programming effort. All you provide is the function to be
optimized and start values, and Optimization does the rest.
Features
- More than 25 options
can be easily specified by the user to control the
optimization
- Descent algorithms include:
BFGS, DFP, Newton, steepest descent, and PRCG
- Step length methods include:
STEPBT, BRENT, and a step-halving method
- A "switching"
method may also be selected which switches the algorithm during the iterations
according to two criteria: number of iterations, or failure of the function
to decrease within a tolerance
Improved Algorithm
Optimization implements the numerically superior Cholesky factorization, solve and
update methods for the BFGS, DFP, and Newton algorithms. The Hessian, or its
estimate, are updated rather than the inverse of the Hessian, and the descent
is computed using a solve. This results in better accuracy and improved convergence
over previous methods.
Platform: Windows, Mac, Linux and Solaris.
Requirements: GAUSS/GAUSS Light version 8.0 or higher.
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