For decades, the Golden State Killer avoided capture despite leaving DNA at multiple crime scenes. Traditional databases failed, fingerprints led nowhere, and the case went cold. Everything changed ...

This paper presents a new three-term hybrid conjugate gradient projection method for handling large-scale convex-constrained nonlinear monotone equations that are prevalent in fields such as ...

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 ...

For difficult problems, in our context, problems on highly deformed meshes, the Conjugate Gradient (CG) method may converge slowly, because the linear system is very poorly conditioned, and expensive ...

Abstract: Conjugate gradient techniques are widely used to solve unconstrained optimization issues. The accelerated conjugate gradient approach provides superior numerical effects for the ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...

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 ...

Secret sprawl, where companies store authentication credentials and similar sensitive data across multiple locations, is a real and growing problem for any company wanting to avert a security breach.

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 ...

In this paper, three new hybrid nonlinear conjugate gradient methods are presented, which produce suf?cient descent search direction at every iteration. This property is independent of any line search ...