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CPSR (Cone Program Solution Refinement)
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cpsr (Cone Program Solution Refinement)

cpsr is a Python library for the iterative improvement, or refinement, of a primal-dual solution, or a certificate of unboundedness or infeasibility, of a cone, or convex, program. It operates by differentiating the conic optimality conditions, and so it can also be used for calculus with conic programs.


Given an approximate solution (or certificate), meaning one for which the optimality conditions don't hold exactly, cpsr produces a new solution for which the norm of the violations of the primal and dual systems, and the duality gap, is smaller.

Mathematics. It does so by locally linearizing the operator 𝒩 (z) ∈ 𝗥^(n), the concatenation of the violations of the primal and dual systems of the problem, and the duality gap, for any approximate primal-dual solution represented by z ∈ 𝗥^(n), via an embedding of the conic optimality conditions; z can also represent a certificate, and in that case 𝒩 (z) is the violation of its (primal or dual) system, concatenated with zero. So, 𝒩 (z) = 0 if and only if z is an exact primal-dual solution or certificate, meaning one for which the conic optimality conditions, or the certificate conditions, are satisfied within machine precision. cpsr proceeds iteratively, using at each steps the current value of 𝒩 (z) and the derivative matrix 𝗗𝒩 (z) to (approximately) solve a linear system that locally approximates the conic optimality conditions.

Matrix-free. cpsr is a matrix-free solver, meaning that it does not store or invert the derivative matrix 𝗗𝒩 (z). This allows it to scale to very large problems. Essentially, if you are able to load the problem data in memory, then cpsr can solve it, using O(n) additional memory, where n is the size of a primal-dual solution.

Iterative solution. cpsr uses LSQR, an iterative linear system solver, to approximately solve a system that locally approximates the conic optimality conditions. The number of LSQR iterations is chosen by the user (by default, for small problems, 30), as is the number of cpsr iterations (by default, for small problems, 2).

Problem classes. cpsr can currently solve cone programs whose cone constraints are products of the zero cone, the non-negative orhant, and any number of second-order cones, exponential cones, and semidefinite cones.

Paper. A much more detailed description of the algorithm used is provided in the accompanying paper. I show the experiments described in the paper in the Jupyter notebook examples/experiments.ipynb.

Dependencies. cpsr depends on numpy for vector arithmetics, scipy for sparse linear algebra, and numba for just-in-time code compilation. It currently runs on a single thread, on CPU.

Future. I plan to rewrite the core library in C, support problems whose data is provided as an abstract linear operator, and (possibly) provide a distributed implementation of the cone projections and the sparse matrix multiplications, either on multiple threads or on GPU. I also plan to release interfaces to other scientific programming languages.


To install, execute in a terminal:

pip install cpsr

cvxpy interface

cpsr can be used in combination with cvxpy, via the cpsr.cvxpy_solve method. An example follows.

import numpy as np
import cvxpy as cp
import cpsr

n = 5
X = cp.Variable((n,n))

problem = cp.Problem(objective = cp.Minimize(cp.norm(X - np.random.randn(n, n))), 
                     constraints = [X @ np.random.randn(n) == np.random.randn(n)])

cpsr.cvxpy_solve(problem, presolve=True, verbose=False)
print('norm of the constraint error, solving with SCS and then refining with CPSR: %.2e' % 

cpsr.cvxpy_solve(problem, verbose=False)
print('norm after refining with CPSR again: %.2e' % 

It has the following output. (Machine precision is around 1.11e-16.)

norm of the constraint error, solving with SCS and then refining with CPSR: 1.48e-11
norm after refining with CPSR again: 5.21e-16
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