1. An optimization method, comprising: determining a best gradient as a sparse direction in a function having a plurality of parameters, the sparse direction including a direction that maximizes change of the function as determined by an optimization method where a sparse direction is defined from sparse regularized constraints; determining a best step size along the sparse direction by finding magnitudes of entries of direction by solving an optimization problem that is constrained to the sparse direction; and recursively refining a solution to the optimization problem using a processor and storage media until a threshold constraint is met.