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While Large Language Models have achieved notable success on formal mathematics benchmarks such as MiniF2F, it remains unclear whether these results stem from genuine logical reasoning or semantic pattern matching against pre-training data.…

Machine Learning · Computer Science 2026-05-04 Lixing Li

Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to…

Software Engineering · Computer Science 2019-11-21 Jingyi Wang , Jun Sun , Shengchao Qin , Cyrille Jegourel

The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use-cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information…

Quantum Physics · Physics 2019-11-20 Agnes Valenti , Evert van Nieuwenburg , Sebastian Huber , Eliska Greplova

Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas. We consider the problem of learning LTL…

Artificial Intelligence · Computer Science 2026-01-22 Ritam Raha , Rajarshi Roy , Nathanaël Fijalkow , Daniel Neider

Verification of algorithms and data structures utilized in modern autonomous and semi-autonomous vehicles for land, sea, air, and space presents a significant challenge. Autonomy algorithms, e.g., route planning, pattern matching, and…

Logic in Computer Science · Computer Science 2018-10-11 David Hardin , Konrad Slind

In recent years, several algorithms, which approximate matrix decomposition, have been developed. These algorithms are based on metric conservation features for linear spaces of random projection types. We show that an i.i.d sub-Gaussian…

Numerical Analysis · Mathematics 2016-02-11 Yariv Aizenbud , Amir Averbuch

Over a decade ago, it was demonstrated that quantum computing has the potential to revolutionize numerical linear algebra by enabling algorithms with complexity superior to what is classically achievable, e.g., the seminal HHL algorithm for…

Quantum Physics · Physics 2025-11-11 Liron Mor Yosef , Haim Avron

Let K be a number field, let A be a finite dimensional semisimple K-algebra and let Lambda be an O_K-order in A. It was shown in previous work that, under certain hypotheses on A, there exists an algorithm that for a given (left)…

Number Theory · Mathematics 2020-03-03 Tommy Hofmann , Henri Johnston

A Random SubMatrix method (RSM) is proposed to calculate the low-rank decomposition of large-scale matrices with known entry percentage \rho. RSM is very fast as the floating-point operations (flops) required are compared favorably with the…

Numerical Analysis · Computer Science 2015-10-28 Yiguang Liu

Ever since entanglement was identified as a computational and cryptographic resource, effort has been made to find an efficient way to tell whether a given density matrix represents an unentangled, or separable, state. Essentially, this is…

Data Structures and Algorithms · Computer Science 2007-05-23 Lawrence M. Ioannou

Quantum algorithms can be analyzed in a query model to compute Boolean functions where input is given in a black box and the aim is to compute function value for arbitrary input using as few queries as possible. We concentrate on quantum…

Quantum Physics · Physics 2009-04-23 Alina Vasilieva

An algorithm for embedding finite dimensional Lie algebras into Lie algebras of vector fields (and Lie superalgebras into Lie superalgebras of vector fields) is offered in a way applicable over ground fields of any characteristic. The…

Representation Theory · Mathematics 2009-11-11 Irina Shchepochkina

A novel method of determining which Dynkin diagrams represent simple finite-dimensional Lie algebras over $\mathbb{C}$ is presented. It is based on a condition that is both necessary and sufficient for a suitably defined Cartan matrix to be…

Mathematical Physics · Physics 2025-10-23 Kai Neergård

Many areas of machine learning and science involve large linear algebra problems, such as eigendecompositions, solving linear systems, computing matrix exponentials, and trace estimation. The matrices involved often have Kronecker,…

Machine Learning · Computer Science 2023-11-30 Andres Potapczynski , Marc Finzi , Geoff Pleiss , Andrew Gordon Wilson

We consider the problem of reconstructing a low rank matrix from a subset of its entries and analyze two variants of the so-called Alternating Minimization algorithm, which has been proposed in the past. We establish that when the…

Machine Learning · Statistics 2016-09-21 David Gamarnik , Sidhant Misra

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its…

Data Structures and Algorithms · Computer Science 2025-03-07 Xin Hong , Dingchao Gao , Sanjiang Li , Shenggang Ying , Mingsheng Ying

We study tight bounds and fast algorithms for LCLMs of several linear differential operators with polynomial coefficients. We analyze the arithmetic complexity of existing algorithms for LCLMs, as well as the size of their outputs. We…

Symbolic Computation · Computer Science 2013-06-19 Alin Bostan , Frédéric Chyzak , Ziming Li , Bruno Salvy

A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time of at most a polynomial factor. This may not…

Quantum Physics · Physics 2017-02-20 Peter W. Shor

We consider two basic algorithmic problems concerning tuples of (skew-)symmetric matrices. The first problem asks to decide, given two tuples of (skew-)symmetric matrices $(B_1, \dots, B_m)$ and $(C_1, \dots, C_m)$, whether there exists an…

Data Structures and Algorithms · Computer Science 2019-02-08 Gábor Ivanyos , Youming Qiao

Lie group theory states that knowledge of a $m$-parameters solvable group of symmetries of a system of ordinary differential equations allows to reduce by $m$ the number of equations. We apply this principle by finding some \emph{affine…

Symbolic Computation · Computer Science 2007-06-13 Alexandre Sedoglavic