The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of ...

Abstract: In this paper, we propose a general solution for nonnegative matrix factorization (NMF), a technique widely used in the field of machine learning. NMF is a type of matrix factorization used ...

Abstract: Large-scale multi-objective optimization problems (LSMOPs) pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.

% Subproblem solver for ARC based on a nonlinear conjugate gradient method. % where eta is a tangent vector at x on the manifold given by problem.M, % g = grad is a tangent vector at x, H[eta] is the ...

This is the AM-SURE Summer Research at NYU Courant. The research will be conducted under the supervision of Florian Wechsung. The project will focus on developing a faster converging algorithm for ...

The conjugate gradient iteration is the "original" Krylov subspace iteration, the most famous of these methods and one of the mainstays of scientific computing. Discovered by Hestenes and Stiefel in ...

Featuring copious illustrations and structured exercises, this valuable introductory text presents a rigorous account of the fundamentals of numerical analysis of both ordinary and partial ...