CCQPpy

CCQPpy is an open source Python library of algorithms to solve the convex constrained quadratic problem. It comes with a systematic comparison of various algorithms for solving convex constrained quadratic programming problems with convex feasible set.

It provides:

• Standardized implementations of modern algorithms
  1. PGD
  2. APGD
  3. BBPGD
  4. SPG
  5. MPRGP-BB
• A variety of convex sets
  1. Unconstrained
  2. Upper/lower bound
  3. Box
  4. Cone
  5. Disjoint Union
• Benchmarks

Installiation:

Can be installed with,

pip install ccqppy

or

git clone https://github.com/palmerb4/CCQPpy.git

Example Usage:

import numpy as np
from ccqppy import solvers
from ccqppy import solution_spaces as ss

A = np.array([[2, -1, 0], [-1, 2, -1], [0, -1, 2]])
exact_x = np.array([1, 0, 1])
b = -A.dot(exact_x)
convex_proj_op = ss.BoxProjOp(3,np.array([-2,-2,-4]),np.array([2,2,5]))

desired_tol = 1e-10
max_mv_mults = 5000
solver = solvers.CCQPSolverSPG(desired_tol, max_mv_mults)
result = solver.solve(A, b, convex_proj_op=convex_proj_op)

print("Solution:\t",result.solution)
print("Exact solution:\t",exact_x)
print("Converged?\t",result.solution_converged)
print("Solution time:\t",result.solution_time)
print("Residual:\t",result.solution_residual)
print("Number of matrix, vector multiplications:\t",result.solution_num_matrix_vector_multiplications)

solving SPG
Solution:	 [ 1.0000000e+00 -5.5187636e-11  1.0000000e+00]
Exact solution:	 [1 0 1]
Converged?	 True
Solution time:	 0.0059010982513427734
Residual:	 7.804688171132893e-11
Number of matrix, vector multiplications:	 86