arXiv subjects

Matthew Aldridge

Publications and source records attributed to Matthew Aldridge.

At least 19 recordsLinked to original sources

arXiv2026

Projected Variational Quantum Extragradient for Zero-Sum Games

We propose a projected variational quantum extragradient (VQEG) framework for computing approximate Nash equilibria in two-player zero-sum matrix games. Mixed strategies are parameterized as Born distributions of parameterized quantum circuits (PQCs), transforming the classical bilinear saddle point problem into a smooth but generally minmax optimization in circuit-parameter space. The expected payoff is expressed as the expectation of a diagonal observable, enabling gradient evaluation via the parameter shift rule and compatibility with shot based quantum hardware. To support arbitrary game sizes, we introduce a dominated embedding that maps (m,n) games to qubit-compatible power-of-two dimensions while preserving equilibrium structure. We then develop a projected extragradient method using stochastic gradient estimates derived from finite measurement shots, and establish variance bounds scaling as O(1/S) with respect to the number of measurement shots S, along with convergence to approximate first-order stationarity under standard assumptions. Since stationarity does not guarantee equilibrium optimality, we evaluate performance using the game-space Nash gap. Numerical results demonstrate high-precision solutions on structured instances up to 32x32, while highlighting challenges in unstructured settings.

eess.SY↗

arXiv2025

Marking and re-marking

A random number of items each independently marked with one of a collection of colours gives rise to the multinomial marking, which generalises binomial thinning. A multivariate version, where previously marked items are then re-marked, has similar properties to taking a linear transformation of a random vector.

math.PR↗

arXiv2025

Discrete broadly stable distributions

A random variable X is strictly stable if a sum of independent copies of X has the same distribution as X up to scaling, and is stable (in the broad sense) if the sum has the same distribution as X up to both scaling and shifting. Steutel and Van Harn studied 'discrete strict stability' for random variables taking values in the non-negative integers, replacing scaling by the thinning operation. We extend this to 'discrete stability' (in the broad sense), where shifting is replaced by the addition of an independent Poisson random variable. The discrete stable distributions are 'Poisson-delayed Sibuya' distributions, which include the Poisson and Hermite distributions. Similar results have been proved independently by Townes (arXiv:2509.05497).

math.PR↗

arXiv2025

The law of thin processes: a law of large numbers for point processes

If you take a superposition of n IID copies of a point process and thin that by a factor of 1/n, then the resulting process tends to a Poisson point process as n tends to infinity. We give a simple proof of this result that highlights its similarity to the law of large numbers and to the law of thin numbers of Harremo\"es et al.

math.PR↗

arXiv2023

Small error algorithms for tropical group testing

We consider a version of the classical group testing problem motivated by PCR testing for COVID-19. In the so-called tropical group testing model, the outcome of a test is the lowest cycle threshold (Ct) level of the individuals pooled within it, rather than a simple binary indicator variable. We introduce the tropical counterparts of three classical non-adaptive algorithms (COMP, DD and SCOMP), and analyse their behaviour through both simulations and bounds on error probabilities. By comparing the results of the tropical and classical algorithms, we gain insight into the extra information provided by learning the outcomes (Ct levels) of the tests. We show that in a limiting regime the tropical COMP algorithm requires as many tests as its classical counterpart, but that for sufficiently dense problems tropical DD can recover more information with fewer tests, and can be viewed as essentially optimal in certain regimes.

cs.IT↗

arXiv2022

Conservative two-stage group testing in the linear regime

Inspired by applications in testing for Covid-19, we consider a variant of two-stage group testing called "conservative" (or "trivial") two-stage testing, where every item declared to be defective must be definitively confirmed by being tested by itself in the second stage. We study this in the linear regime where the prevalence is fixed while the number of items is large. We study various nonadaptive test designs for the first stage, and derive a new lower bound for the total number of tests required. We find that a first-stage design as studied by Broder and Kumar (arXiv:2004.01684) with constant tests per item and constant items per test is extremely close to optimal for all prevalences, and is optimal in the limit as the prevalence tends to zero. Simulations back up the theoretical results.

stat.AP↗

arXiv2021

Pooled testing and its applications in the COVID-19 pandemic

When testing for a disease such as COVID-19, the standard method is individual testing: we take a sample from each individual and test these samples separately. An alternative is pooled testing (or "group testing"), where samples are mixed together in different pools, and those pooled samples are tested. When the prevalence of the disease is low and the accuracy of the test is fairly high, pooled testing strategies can be more efficient than individual testing. In this chapter, we discuss the mathematics of pooled testing and its uses during pandemics, in particular the COVID-19 pandemic. We analyse some one- and two-stage pooling strategies under perfect and imperfect tests, and consider the practical issues in the application of such protocols.

stat.AP↗

arXiv2021

Pooled testing to isolate infected individuals

The usual problem for group testing is this: For a given number of individuals and a given prevalence, how many tests T* are required to find every infected individual? In real life, however, the problem is usually different: For a given number of individuals, a given prevalence, and a limited number of tests T much smaller than T*, how can these tests best be used? In this conference paper, we outline some recent results on this problem for two models. First, the "practical" model, which is relevant for screening for COVID-19 and has tests that are highly specific but imperfectly sensitive, shows that simple algorithms can be outperformed at low prevalence and high sensitivity. Second, the "theoretical" model of very low prevalence with perfect tests gives interesting new mathematical results.

stat.AP↗

arXiv2020

Rates of adaptive group testing in the linear regime

We consider adaptive group testing in the linear regime, where the number of defective items scales linearly with the number of items. We analyse an algorithm based on generalized binary splitting. Provided fewer than half the items are defective, we achieve rates of over 0.9 bits per test for combinatorial zero-error testing, and over 0.95 bits per test for probabilistic small-error testing.

cs.IT↗

arXiv2020

On the All-Or-Nothing Behavior of Bernoulli Group Testing

In this paper, we study the problem of non-adaptive group testing, in which one seeks to identify which items are defective given a set of suitably-designed tests whose outcomes indicate whether or not at least one defective item was included in the test. The most widespread recovery criterion seeks to exactly recover the entire defective set, and relaxed criteria such as approximate recovery and list decoding have also been considered. In this paper, we study the fundamental limits of group testing under the significantly relaxed {\em weak recovery} criterion, which only seeks to identify a small fraction (e.g., $0.01$) of the defective items. Given the near-optimality of i.i.d.~Bernoulli testing for exact recovery in sufficiently sparse scaling regimes, it is natural to ask whether this design additionally succeeds with much fewer tests under weak recovery. Our main negative result shows that this is not the case, and in fact, under i.i.d.~Bernoulli random testing in the sufficiently sparse regime, an {\em all-or-nothing} phenomenon occurs: When the number of tests is slightly below a threshold, weak recovery is impossible, whereas when the number of tests is slightly above the same threshold, high-probability exact recovery is possible. In establishing this result, we additionally prove similar negative results under Bernoulli designs for the weak detection problem (distinguishing between the group testing model vs.~completely random outcomes) and the problem of identifying a single item that is definitely defective. On the positive side, we show that all three relaxed recovery criteria can be attained using considerably fewer tests under suitably-chosen non-Bernoulli designs.

cs.IT↗

arXiv2019

Group Testing: An Information Theory Perspective

The group testing problem concerns discovering a small number of defective items within a large population by performing tests on pools of items. A test is positive if the pool contains at least one defective, and negative if it contains no defectives. This is a sparse inference problem with a combinatorial flavour, with applications in medical testing, biology, telecommunications, information technology, data science, and more. In this monograph, we survey recent developments in the group testing problem from an information-theoretic perspective. We cover several related developments: efficient algorithms with practical storage and computation requirements, achievability bounds for optimal decoding methods, and algorithm-independent converse bounds. We assess the theoretical guarantees not only in terms of scaling laws, but also in terms of the constant factors, leading to the notion of the {\em rate} of group testing, indicating the amount of information learned per test. For the noiseless setting, we present a series of results leading to optimal rates, which in turn imply optimality and suboptimality results of various algorithms depending on the sparsity regime. We also survey analogous developments in noisy settings. In addition, we survey results concerning a number of variations on the standard group testing problem, including approximate recovery criteria, adaptive algorithms with a limited number of stages, sublinear-time algorithms, and settings with additional prior information, among others.

cs.IT↗

arXiv2018

Performance of group testing algorithms with near-constant tests-per-item

We consider the nonadaptive group testing with N items, of which $K = Θ(N^θ)$ are defective. We study a test design in which each item appears in nearly the same number of tests. For each item, we independently pick L tests uniformly at random with replacement, and place the item in those tests. We analyse the performance of these designs with simple and practical decoding algorithms in a range of sparsity regimes, and show that the performance is consistently improved in comparison with standard Bernoulli designs. We show that our new design requires 23% fewer tests than a Bernoulli design when paired with the simple decoding algorithms known as COMP and DD. This gives the best known nonadaptive group testing performance for $θ> 0.43$, and the best proven performance with a practical decoding algorithm for all $θ\in (0,1)$. We also give a converse result showing that the DD algorithm is optimal for these designs when $θ> 1/2$.

cs.IT↗

arXiv2018

Individual testing is optimal for nonadaptive group testing in the linear regime

We consider nonadaptive probabilistic group testing in the linear regime, where each of n items is defective independently with probability p in (0,1), and p is a constant independent of n. We show that testing each item individually is optimal, in the sense that with fewer than n tests the error probability is bounded away from zero.

cs.IT↗

arXiv2017

The capacity of Bernoulli nonadaptive group testing

We consider nonadaptive group testing with Bernoulli tests, where each item is placed in each test independently with some fixed probability. We give a tight threshold on the maximum number of tests required to find the defective set under optimal Bernoulli testing. Achievability is given by a result of Scarlett and Cevher; here we give a converse bound showing that this result is best possible. Our new converse requires three parts: a typicality bound generalising the trivial counting bound, a converse on the COMP algorithm of Chan et al, and a bound on the SSS algorithm similar to that given by Aldridge, Baldassini, and Johnson. Our result has a number of important corollaries, in particular that, in denser cases, Bernoulli nonadaptive group testing is strictly worse than the best adaptive strategies.

cs.IT↗

arXiv2017

On the optimality of some group testing algorithms

We consider Bernoulli nonadaptive group testing with $k = Θ(n^θ)$ defectives, for $θ\in (0,1)$. The practical definite defectives (DD) detection algorithm is known to be optimal for $θ\geq 1/2$. We give a new upper bound on the rate of DD, showing that DD is strictly suboptimal for $θ< 0.41$. We also show that the SCOMP algorithm and algorithms based on linear programming achieve a rate at least as high as DD, so in particular are also optimal for $θ\geq 1/2$.

cs.IT↗

arXiv2016

Improved group testing rates with constant column weight designs

We consider nonadaptive group testing where each item is placed in a constant number of tests. The tests are chosen uniformly at random with replacement, so the testing matrix has (almost) constant column weights. We show that performance is improved compared to Bernoulli designs, where each item is placed in each test independently with a fixed probability. In particular, we show that the rate of the practical COMP detection algorithm is increased by 31% in all sparsity regimes. In dense cases, this beats the best possible algorithm with Bernoulli tests, and in sparse cases is the best proven performance of any practical algorithm. We also give an algorithm-independent upper bound for the constant column weight case; for dense cases this is again a 31% increase over the analogous Bernoulli result.

cs.IT↗

arXiv2014

Almost Separable Matrices

An $m \times n$ matrix $\mathsf{A}$ with column supports $\{S_i\}$ is $k$-separable if the disjunctions $\bigcup_{i \in \mathcal{K}} S_i$ are all distinct over all sets $\mathcal{K}$ of cardinality $k$. While a simple counting bound shows that $m > k \log_2 n/k$ rows are required for a separable matrix to exist, in fact it is necessary for $m$ to be about a factor of $k$ more than this. In this paper, we consider a weaker definition of `almost $k$-separability', which requires that the disjunctions are `mostly distinct'. We show using a random construction that these matrices exist with $m = O(k \log n)$ rows, which is optimal for $k = O(n^{1-β})$. Further, by calculating explicit constants, we show how almost separable matrices give new bounds on the rate of nonadaptive group testing.

math.CO↗

arXiv2013

Group testing algorithms: bounds and simulations

We consider the problem of non-adaptive noiseless group testing of $N$ items of which $K$ are defective. We describe four detection algorithms: the COMP algorithm of Chan et al.; two new algorithms, DD and SCOMP, which require stronger evidence to declare an item defective; and an essentially optimal but computationally difficult algorithm called SSS. By considering the asymptotic rate of these algorithms with Bernoulli designs we see that DD outperforms COMP, that DD is essentially optimal in regimes where $K \geq \sqrt N$, and that no algorithm with a nonadaptive Bernoulli design can perform as well as the best non-random adaptive designs when $K > N^{0.35}$. In simulations, we see that DD and SCOMP far outperform COMP, with SCOMP very close to the optimal SSS, especially in cases with larger $K$.

cs.IT↗