Optimization

Contains classes and methods to implement the abstract concept of an optimization algorithm.

Classes
class	gurls::RLSAuto< T >
	RLSAuto is the sub-class of Optimizer that implements automatic selection of primal/dual procedure. More...
class	gurls::RLSDual< T >
	RLSDual is the sub-class of Optimizer that implements RLS with the dual formulation. More...
class	gurls::RLSDualr< T >
	RLSDualr is the sub-class of Optimizer that implements RLS with the dual formulation, using a randomized version of SVD. More...
class	gurls::RLSGPRegr< T >
	RLSGPRegr is the sub-class of Optimizer that implements GP inference. More...
class	gurls::RLSPegasos< T >
	RLSPegasos is the sub-class of Optimizer that implements the Pegaosos algorithm. More...
class	gurls::RLSPrimal< T >
	RLSPrimal is the sub-class of Optimizer that implements RLS with the primal formulation. More...
class	gurls::RLSPrimalr< T >
	RLSPrimalr is the sub-class of Optimizer that implements RLS with the primal formulation, using a randomized version of SVD. More...
class	gurls::RLSPrimalRecInit< T >
	RLSPrimalRecInit is the sub-class of Optimizer that implements RLS with the primal formulation. More...
class	gurls::RLSPrimalRecUpdate< T >
	RLSPrimalRecUpdate is the sub-class of Optimizer that implements RLS with the primal formulation. More...
class	gurls::RLSRandFeats< T >
	RLSRandFeats is the sub-class of Optimizer that computes a classifier for the primal formulation of RLS using the Random Features approach proposed in: Ali Rahimi, Ben Recht; Random Features for Large-Scale Kernel Machines; in Neural Information Processing Systems (NIPS) 2007. More...