CVXPY 1.9: Recent Advances in Optimization Modeling Software
For optimization practitioners and researchers, CVXPY 1.9 extends the range of solvable problems and accelerates parameterized optimization, though many features are incremental improvements.
CVXPY 1.9 introduces a unified conic quadratic program standard form, a stacked-slices backend for faster parameterized programs, N-dimensional expression support, explicit sparsity, multiple variable attributes, quantum information theory cones/atoms, and disciplined nonlinear programming (DNLP). These advances improve modeling flexibility and computational efficiency.
CVXPY is a Python-embedded domain-specific language for convex optimization that lets users express problems in mathematical notation while the system verifies convexity and reduces valid programs to solver-ready form. This paper reports on the major advances from versions 1.1 through 1.9. These include a unified conic quadratic program (CQP) standard form for canonicalization; a stacked-slices backend that accelerates parameterized programs; first-class support for N-dimensional expressions; explicit sparsity for variables; support for multiple variable attributes; cones/atoms relevant to quantum information theory; and the introduction of disciplined nonlinear programming (DNLP). We outline the design, algorithms, and modeling consequences of these features.