From benchmark to algorithms.

SmartSolveAI's methodology begins by benchmarking choices across the linear-algebra design space — including algorithm classes, mixed-precision strategies, sparsity structures, memory layouts, and hardware backends. It then uses the best-performing choices — evaluated by metrics such as accuracy, runtime, and memory usage — as input to an AI-assisted process that generates (1) selection heuristics to dispatch the optimal configuration for a given input, such as a specific preconditioner–solver pair, and (2) specialized variants of state-of-the-art solvers by leveraging information such as the input matrix's pattern and the target hardware.