NNLS

Solve a least squares problem, possibly with nonnegativity constraints, by a modified projected gradient method. That is, find x minimising ||Ax - b||_2 given A^T A and A^T b.

We solve the problem min_x 1/2 x' ata x' - x'atb subject to x >= 0

The method used is similar to one described by Polyak (B. T. Polyak, The conjugate gradient method in extremal problems, Zh. Vychisl. Mat. Mat. Fiz. 9(4)(1969), pp. 94-112) for bound- constrained nonlinear programming. Polyak unconditionally uses a conjugate gradient direction, however, while this method only uses a conjugate gradient direction if the last iteration did not cause a previously-inactive constraint to become active.

minimizeAndReturnState allows users to hot start the solver using initialState. If a initialState is provided and resetState is set to false, the optimizer will hot start using the previous state. By default resetState is true and every time reset will be called on the incoming state