Rust + WASM sublinear-time solver for asymmetric diagonally dominant systems. Exposes Neumann series, push, and hybrid random-walk algorithms with npm/npx CLI and Flow-Nexus HTTP streaming for swarm ...

Motivation: sparse LM optimizer relies on a sparse Ax = b solver Hi! We are working on a sparse Levenberg–Marquardt optimizer, and we have already sparsified the Jacobian matrix and A matrix (derived ...

Quantum computing is an emerging field that has had a significant impact on optimization. Among the diverse quantum algorithms, quantum gradient descent has become a prominent technique for solving ...

Scouting gradients can simplify LC method development. Here’s what you need to get started using them. With so many options for method parameters to adjust during method development, identifying a ...

Abstract: The conjugate-gradient method (CGM) is a traditional approach to solve the inverse scattering problem (ISP). This method requires the solution of both the forward problem and the calculation ...

where and for, are random matrices and vectors. When, stochastic generalized linear complementarity problems reduce to the classic Stochastic Linear Complementarity Problems (SLCP), which has been ...

A class of finite step iterative methods, conjugate gradients, for the solution of an operator equation, is presented on this paper to solve electromagnetic scattering. The method of generalized ...

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 ...