cuopt-numerical-optimization-api-python
PopularSolve LP, MILP, QP (beta) with cuOpt Python API — linear/quadratic objectives, integer variables, scheduling, portfolio, least squares.
Solve LP, MILP, QP (beta) with cuOpt Python API — linear/quadratic objectives, integer variables, scheduling, portfolio, least squares.
cuOpt Numerical Optimization Skill (Python)
Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver. The Python API surface (Problem, SolverSettings, solve) is shared across all three problem classes — only the objective form and a few rules change.
Before You Start
Use a formulation summary (parameters, constraints, decisions, objective) if available; otherwise ask for decision variables, objective, and constraints. Then confirm problem type (LP / MILP / QP — see below) and variable types.
Choosing LP vs MILP vs QP
Decide from the objective and variables:
If the objective is...
And variables are...
Use
Linear (sum of c_i * x_i)
All continuous
LP
Linear
Some integer or binary
MILP
Has squared (x*x) or cross (x*y) terms
Continuous (integer QP not supported)
QP (beta)
