The CVXPY documentation is at cvxpy.org.
Try the new, improved CVXPY 1.0, available here. Please report any bugs you find!
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 where the variable is constrained by lower and upper bounds:
from cvxpy import * import numpy # Problem data. m = 30 n = 20 numpy.random.seed(1) A = numpy.random.randn(m, n) b = numpy.random.randn(m) # Construct the problem. x = Variable(n) objective = Minimize(sum_squares(A*x - b)) constraints = [0 <= x, x <= 1] prob = Problem(objective, constraints) # The optimal objective 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)
CVXPY was designed and implemented by Steven Diamond, with input from Stephen Boyd and Eric Chu.
A tutorial and other documentation can be found at cvxpy.org.