Welcome to CVXPY 1.1¶
Convex optimization, for everyone.
CVXPY is a Python-embedded modeling language for convex optimization problems. It allows you to express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers.
For example, the following code solves a least-squares problem with box constraints:
import cvxpy as cp import numpy as np # Problem data. m = 30 n = 20 np.random.seed(1) A = np.random.randn(m, n) b = np.random.randn(m) # Construct the problem. x = cp.Variable(n) objective = cp.Minimize(cp.sum_squares(A @ x - b)) constraints = [0 <= x, x <= 1] prob = cp.Problem(objective, constraints) # The optimal objective value is returned by `prob.solve()`. result = prob.solve() # The optimal value for x is stored in `x.value`. print(x.value) # The optimal Lagrange multiplier for a constraint is stored in # `constraint.dual_value`. print(constraints.dual_value)
This short script is a basic example of what CVXPY can do; in addition to convex programming, CVXPY also supports a generalization of geometric programming.
We are building a CVXPY community on Discord. Join the conversation!
CVXPY v1.1.0 has been released. This version makes repeatedly canonicalizing parametrized problems much faster than before, allows differentiating the map from parameters to optimal solutions, and introduces some new atoms. See Changes to CVXPY for more information.
CVXPY relies on the open source solvers ECOS, OSQP, and SCS. Additional solvers are supported, but must be installed separately. For background on convex optimization, see the book Convex Optimization by Boyd and Vandenberghe.
CVXPY is a community project, built from the contributions of many researchers and engineers.
CVXPY is developed and maintained by Steven Diamond, Akshay Agrawal, and Riley Murray, with many others contributing significantly. A non-exhaustive list of people who have shaped CVXPY over the years includes Stephen Boyd, Eric Chu, Robin Verschueren, Bartolomeo Stellato, Jaehyun Park, Enzo Busseti, AJ Friend, Judson Wilson, and Chris Dembia.