In this study, we focus on investigating a nonsmooth convex optimization problem involving the l 1-norm under a non-negative constraint, with the goal of developing an inverse-problem solver for image ...

GPU-accelerated linear solvers based on the conjugate gradient (CG) method, supporting NVIDIA and AMD GPUs with GPU-aware MPI, NCCL, RCCL or NVSHMEM ...

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 ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple. Trump's sons distance themselves from new Trump-branded ...

Dive deep into Nesterov Accelerated Gradient (NAG) and learn how to implement it from scratch in Python. Perfect for improving optimization techniques in machine learning! 💡🔧 #NesterovGradient ...

Abstract: This paper presents a new version of conjugate gradient (CG) algorithm, which depends mainly on the sign of direction of search, the algorithm is called SDCGA. In addition, the algorithm is ...

ABSTRACT: In this paper, we consider a more general bi-level optimization problem, where the inner objective function is consisted of three convex functions, involving a smooth and two non-smooth ...

Abstract: The paper describes new conjugate gradient algorithms which use preconditioning. The algorithms are intended for general nonlinear unconstrained problems. In order to speed up the ...