Convex Optimization, Constrained Optimization, Lagrange Multiplier, Pangpuriyesuperai, Superaiengineer.
cvxpy as cp import numpy as np # Generate a random non-trivial linear program. m = 15 n = 10 np.random.seed(1) s0 = np.random.randn(m) lamb0 = np.maximum(-s0, 0) s0 = np.maximum(s0, 0) x0 = np.random.randn(n) A = np.random.randn(m, n) b = A@x0 + s0 c = -A.T@lamb0 # Define and solve the convex