minimal gradient

We propose a minimal-gradient subspace method for unconstrained optimization. For strictly convex quadratics, conjugate gradient can be interpreted as exact minimization over a two-dimensional affine subspace. We use the same reduced subspace in the nonlinear case, but compute a trial step by minimizing a local model of the next gradient norm. For SPD quadratics, every … Read more