Description
Solves convex optimization problems using the Splitting Conic Solver from Python. It is useful for scientific computing, control, finance, machine learning, and other workflows that formulate problems as cone programs.
Researchers and developers use it as a numerical library. Solver results depend on modeling choices, scaling, tolerances, and convergence status, so inspect diagnostics before trusting an optimization result.