nncg 0.2.1

Non-negative conjugate gradients: bound-constrained SPD quadratics by a guarded active-set loop

➕ nncg — Non-Negative Conjugate Gradients

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Quick Links: 📄 Paper • 🐛 Report Bug • 💡 Request Feature

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📋 Overview

nncg solves the strictly convex non-negative quadratic program

$$\min_{x \geq 0}\ \tfrac{1}{2} x^\top A x - b^\top x, \qquad A \succ 0,$$

and its equality-augmented variant with a general linear system $Bx = c$, by wrapping matrix-free conjugate gradients in a primal-dual active-set loop. The working-set toggles are the principal pivots of the linear complementarity problem $\mathrm{LCP}(A, -b)$; guarding the fast block-pivot path with a least-index Bland fallback gives unconditional finite termination at the unique global minimiser — no non-degeneracy assumption.

This is the reference implementation of the paper Non-Negative Conjugate Gradients (Schmelzer & Stoll), developed in Jebel-Quant/mean_variance_solvers. The paper's numerical study doubles as this package's test suite: planted-optimum recovery across condition numbers, the equality-augmented solve for $p \in {1, 3, 8}$, CG-vs-exact free-set trajectory agreement (the inexactness lemma), warm-started parameter sweeps, and the adversarial anti-correlated family on which the unguarded batch path provably cycles and the fallback terminates.

The quadratic term enters as a cvx.linalg.SymmetricOperator: wrap an explicit SPD array in DenseOperator. When $A = M^\top M$ is a Gram matrix, pass GramOperator(M, ridge) and the inner solves need only products with $M$ — the $n \times n$ matrix is never formed and working memory is $O(n)$.

📦 Installation

pip install nncg

🚀 Quickstart

import numpy as np
from cvx.linalg import DenseOperator, GramOperator
from nncg import kkt_violation, solve_nnqp, solve_nnqp_eq

# a random SPD problem with condition number 1e4
rng = np.random.default_rng(0)
Q, _ = np.linalg.qr(rng.standard_normal((200, 200)))
A = (Q * np.geomspace(1.0, 1e4, 200)) @ Q.T
b = rng.standard_normal(200)
op = DenseOperator(A)                     # the solvers take a SymmetricOperator

res = solve_nnqp(op, b)
assert res.converged                      # stopped on the KKT certificate
assert kkt_violation(op, b, res.x) < 1e-6 # zero certifies the global minimiser

# equality-augmented: minimise subject to x >= 0 and B x = c
B = np.ones((1, 200))                     # p = 1: the budget 1'x = 1
res_eq = solve_nnqp_eq(op, b, B, np.array([1.0]))
assert res_eq.lam.shape == (1,)           # multiplier, via a p-by-p Schur solve

# warm-start a parametric sweep: support-stable steps take ONE outer step
res2 = solve_nnqp(op, b + 1e-4, warm=(res.free, res.x))

# Gram-structured: A = M'M + I only through products with M — never formed
M = np.random.default_rng(1).standard_normal((50, 200))
res_g = solve_nnqp(GramOperator(M, ridge=1.0), M.T @ np.ones(50))
assert res_g.converged

🔬 The algorithm in one paragraph

Fix a working set of free variables and solve the unconstrained reduced SPD system by CG (matrix-free, $O(\sqrt{\kappa})$ Krylov rate). Push any free variable that returns negative to its bound (primal step); release any bound variable whose reduced gradient is negative (dual step); repeat. Batch exchanges are fast but can cycle; a patience counter falls back to Murty's least-index single pivot, which cannot — hence finite termination without any non-degeneracy hypothesis, and the fallback is provably necessary: on anti-correlated designs (the make_adversarial family in the test suite's tests/problems.py) the unguarded batch path revisits a previously seen working set and loops forever.

📖 Citation

If you use this package in academic work, please cite the paper:

@techreport{schmelzer2026nncg,
  title       = {Non-Negative Conjugate Gradients},
  author      = {Schmelzer, Thomas and Stoll, Martin},
  year        = {2026},
  institution = {Jebel Quant Research and TU Chemnitz},
  url         = {https://github.com/Jebel-Quant/mean_variance_solvers},
}

⚖️ License

MIT — see LICENSE.