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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[0].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.
This git repository holds the latest development version of CVXPY. For installation instructions, see the install guide at cvxpy.org.