CVXPY
CVXPY:
CVXPY is a Python library for specifying and solving convex optimization problems. It is designed to make it easy to construct and solve these problems in a more intuitive and user-friendly manner than directly coding the problem in a lower-level language.
Here’s a simple example of how you might use CVXPY:
import cvxpy as cp
# Create two scalar optimization variables.
x = cp.Variable()
y = cp.Variable()
# Create two constraints.
constraints = [x + y == 1,
x - y >= 1]
# Form objective.
obj = cp.Minimize((x - y)**2)
# Form and solve problem.
prob = cp.Problem(obj, constraints)
prob.solve() # Returns the optimal value.
print("status:", prob.status)
print("optimal value", prob.value)
print("optimal var", x.value, y.value)
In this example, we’re solving the problem of minimizing the square of (x - y) subject to the constraints that x + y == 1 and x - y >= 1.
CVXPY can be used to solve a variety of optimization problems and is particularly useful for convex optimization problems, which are a broad class of problems that are known to be efficiently solvable. CVXPY provides interfaces to various optimization solvers like ECOS, SCS, MOSEK, and CVXOPT.
CVXPY is also able to take advantage of vectorized operations and can handle problems with a mix of continuous and integer variables, making it suitable for a wide range of practical applications.
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