01421nam a2200169 a 4500 20260901030406.0 250101s2020 xx o 000 0 eng d Md Sarowar Morshed Stochastic Steepest Descent Methods for Linear Systems: Greedy Sampling & Momentum arXiv 2020 text online resource Recently proposed adaptive Sketch & Project (SP) methods connect several well-known projection methods such as Randomized Kaczmarz (RK), Randomized Block Kaczmarz (RBK), Motzkin Relaxation (MR), Randomized Coordinate Descent (RCD), Capped Coordinate Descent (CCD), etc. into one framework for solving linear systems. In this work, we first propose a Stochastic Steepest Descent (SSD) framework that connects SP methods with the well-known Steepest Descent (SD) method for solving positive-definite linear system of equations. We then introduce two greedy sampling strategies in the SSD framework that allow us to obtain algorithms such as Sampling Kaczmarz Motzkin (SKM), Sampling Block Kaczmarz (SBK), Sampling Coordinate Descent (SCD), etc. In doing so, we generalize the existing sampling rules into one framework and develop an efficient version of SP methods. Furthermore, we incorporated the Po Open access — freely available to read. https://arxiv.org/pdf/2012.13087v1 Read the full paper (PDF) ERES 700 700 0 0 0 3 GEN MAIN MAIN SCICOMP 2026-09-01 0 YGE000961 2026-09-01 03:04:06 https://arxiv.org/pdf/2012.13087v1 2026-09-01 PAPER