Linear System Solver

Linear system solvers are algorithms designed to efficiently find solutions to systems of linear equations, a fundamental problem across numerous scientific and engineering disciplines. Current research emphasizes improving solver speed and robustness, particularly focusing on techniques like conjugate gradients, accelerated projection-based consensus, and optimized Schwarz domain decomposition methods, often incorporating machine learning for parameter optimization or approximation. These advancements are crucial for accelerating computations in diverse fields, including machine learning, Gaussian process regression, and large-scale graph analysis, where the computational cost of solving large linear systems is a major bottleneck. The development of faster, more robust, and adaptable solvers directly impacts the scalability and efficiency of many scientific and engineering applications.