CVXPY
Convex optimization modeling language for Python — formulate and solve convex optimization problems with a natural mathematical syntax. CVXPY features: cp.Variable() decision variables, cp.Minimize()/cp.Maximize() objectives, cp.Problem() with constraints list, solve() with multiple solver backends (OSQP, SCS, ECOS, GLPK, GUROBI, MOSEK), DCP (Disciplined Convex Programming) validation, cp.atoms for convex functions (cp.norm, cp.sum_squares, cp.log_sum_exp), Parameter for efficient re-solving, warm starting, and mixed-integer support (via GLPK_MI, CPLEX). Used for portfolio optimization, resource allocation, machine learning regularization, and agent decision making under constraints.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Local computation — no network calls during optimization. Commercial solver license keys should be stored in environment variables not code. No data exfiltration risk.
⚡ Reliability
Best When
Portfolio optimization, resource allocation, regularized ML, or any optimization problem that can be expressed as convex — CVXPY's DCP checker guarantees the problem is solvable and selects the best available solver automatically.
Avoid When
Your optimization problem is non-convex, involves large neural networks, or requires large-scale integer programming.
Use Cases
- • Agent portfolio optimization — w = cp.Variable(n); ret = mu.T @ w; risk = cp.quad_form(w, Sigma); prob = cp.Problem(cp.Maximize(ret - gamma*risk), [cp.sum(w)==1, w>=0]); prob.solve(); allocation = w.value — Markowitz portfolio optimization; agent allocates budget across assets maximizing return for given risk tolerance
- • Agent resource allocation — x = cp.Variable(n, nonneg=True); prob = cp.Problem(cp.Minimize(cp.sum_squares(A@x - b)), [C@x <= d]); prob.solve() — least-squares resource allocation with constraints; agent assigns compute/bandwidth/inventory across tasks minimizing waste subject to capacity limits
- • Agent regularized ML — theta = cp.Variable(d); loss = cp.sum_squares(X@theta - y)/n; reg = cp.norm(theta, 1); prob = cp.Problem(cp.Minimize(loss + lam*reg)); prob.solve() — LASSO regression via convex optimization; agent trains sparse model for interpretable feature selection
- • Agent parametric re-solving — lam = cp.Parameter(nonneg=True); lam.value = 0.1; prob.solve(); lam.value = 1.0; prob.solve() — Parameter allows changing values without re-compilation; agent sweeps regularization hyperparameter efficiently without rebuilding problem each time
- • Agent SVM training — xi = cp.Variable(n, nonneg=True); w = cp.Variable(d); b = cp.Variable(); prob = cp.Problem(cp.Minimize(cp.norm(w,2) + C*cp.sum(xi)), [cp.multiply(y, X@w+b) >= 1-xi]); prob.solve() — SVM with slack variables; agent trains classifier via convex optimization for interpretable margin maximization
Not For
- • Non-convex optimization — CVXPY enforces Disciplined Convex Programming (DCP); non-convex objectives raise DCPError; use scipy.optimize or GEKKO for non-convex problems
- • Large-scale deep learning — CVXPY is for small-to-medium convex problems; for neural network training use PyTorch/JAX with gradient descent
- • Integer programming at scale — mixed-integer convex via GLPK_MI is slow for large problems; use OR-Tools or Gurobi for large MIP
Interface
Authentication
No auth — local optimization library. Commercial solvers (Gurobi, MOSEK) require separate license keys.
Pricing
CVXPY is Apache 2.0 licensed. Default solvers are open-source. Gurobi/MOSEK integration is optional and requires their licenses.
Agent Metadata
Known Gotchas
- ⚠ DCP violation raises at solve time not formulation — cp.Problem(cp.Minimize(x**3)) compiles without error but prob.solve() raises DCPError; agent code must catch DCPError and inform user that objective is non-convex; use cp.atoms_functions for convex combinations
- ⚠ Check prob.status before accessing .value — variable.value is None when prob.status is 'infeasible' or 'unbounded'; agent code doing allocation = w.value without status check gets None and fails downstream; always check: if prob.status not in ['optimal', 'optimal_inaccurate']: handle_failure()
- ⚠ Parameter vs Constant for efficient re-solving — using Python floats directly in problem (lam=0.1 as constant) requires full problem recompilation when value changes; cp.Parameter() allows changing .value and re-solving without recompilation; agent hyperparameter search must use Parameter for 10-100x speedup
- ⚠ Solver selection matters for problem type — OSQP default works for QP; SCS needed for semidefinite programs; GLPK_MI for integer programs; wrong solver raises SolverError; agent code for integer/semidefinite problems must specify: prob.solve(solver=cp.SCS)
- ⚠ Numerical precision with 'optimal_inaccurate' — prob.status='optimal_inaccurate' means solution may not satisfy constraints exactly; agent allocation that sums to 1 may get 0.9999 or 1.0001; add tolerance check or tighten solver: prob.solve(eps_abs=1e-8, eps_rel=1e-8)
- ⚠ Variable bounds via nonneg/nonpos not constraints — cp.Variable(n, nonneg=True) is more numerically stable than w = cp.Variable(n) with constraint w >= 0; agent optimization with non-negativity constraints should use nonneg parameter for better solver performance
Alternatives
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Scores are editorial opinions as of 2026-03-06.