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We present a symplectic linear-algebraic proof of the Quantum Singleton Bound for stabiliser quantum error-correcting codes together with a Lean4 formalisation of the linear-algebraic argument. The proof is formulated in the language of…

Quantum Physics · Physics 2026-03-31 Frederick Dehmel , Shilun Li

In this paper, the Lorentz covariance of algorithms is introduced. Under Lorentz transformation, both the form and performance of a Lorentz covariant algorithm are invariant. To acquire the advantages of symplectic algorithms and Lorentz…

Plasma Physics · Physics 2017-09-13 Yulei Wang , Jian Liu , Hong Qin

We formulate and numerically simulate the single control qubit Shor algorithm for the case of static imperfections induced by residual couplings between qubits. This allows us to study the accuracy of Shor's algorithm with respect to these…

Quantum Physics · Physics 2008-12-15 Ignacio Garcia-Mata , Klaus M. Frahm , Dima L. Shepelyansky

Statistical solutions of incompressible Euler describe turbulent dynamics as time-parameterized laws on $L^2$ whose multi-point correlations satisfy an infinite hierarchy of weak identities. Modern generative samplers for PDE forecasting…

Analysis of PDEs · Mathematics 2026-02-24 Victor Armegioiu

We study two fundamental optimization problems: (1) scaling a symmetric positive definite matrix by a positive diagonal matrix so that the resulting matrix has row and column sums equal to 1; and (2) minimizing a quadratic function subject…

Data Structures and Algorithms · Computer Science 2025-04-30 Adrian Vladu

The Symmetric Primal-Dual Symplex Pivot Decision Strategy (spdspds) is a novel iterative algorithm to solve linear programming problems. A symplex pivoting operation is simply an exchange between a basic variable and a non-basic variable,…

Optimization and Control · Mathematics 2026-05-19 Keshava Prasad Halemane

Computing Gaussian ground states via variational optimization is challenging because the covariance matrices must satisfy the uncertainty principle, rendering constrained or Riemannian optimization costly, delicate, and thus difficult to…

Quantum Physics · Physics 2026-01-29 Christopher Willby , Tomohiro Hashizume , Jason Crain , Dieter Jaksch

In this paper, we derive a family of fast and stable algorithms for multiplying and inverting $n \times n$ Pascal matrices that run in $O(n log^2 n)$ time and are closely related to De Casteljau's algorithm for B\'ezier curve evaluation.…

Numerical Analysis · Computer Science 2017-11-23 Samuel F. Potter , Ramani Duraiswami

We consider GL_n(F_q)-analogues of certain factorization problems in the symmetric group S_n: rather than counting factorizations of the long cycle (1, 2, ..., n) given the number of cycles of each factor, we count factorizations of a…

Combinatorics · Mathematics 2016-06-16 Joel Brewster Lewis , Alejandro H. Morales

Incomplete factorizations have long been popular general-purpose algebraic preconditioners for solving large sparse linear systems of equations. Guaranteeing the factorization is breakdown free while computing a high quality preconditioner…

Numerical Analysis · Mathematics 2025-02-04 Jennifer Scott , Miroslav Tůma

In this paper, we mainly study tilt stability and Lipschitz stability of convex optimization problems. Our characterizations are geometric and fully computable in many important cases. As a result, we apply our theory to the group Lasso…

Optimization and Control · Mathematics 2025-02-18 Tran T. A. Nghia

Under reasonable algebraic assumptions and under an infinite second order moment assumption, we show that the logarithm of the norm (log-norm) of a product of random i.i.d. matrices with entries in $\mathbb{R}$ or in any other local field…

Probability · Mathematics 2026-01-09 Axel Péneau

A popular and efficient discretization of evolutions involving the singular $p$-Laplace operator is based on a factorization of the differential operator into a linear part which is treated implicitly and a regularized singular factor which…

Numerical Analysis · Mathematics 2017-12-12 Sören Bartels , Lars Diening , Ricardo H. Nochetto

Latent Dirichlet allocation (LDA) is useful in document analysis, image processing, and many information systems; however, its generalization performance has been left unknown because it is a singular learning machine to which regular…

Statistics Theory · Mathematics 2020-02-21 Naoki Hayashi , Sumio Watanabe

Given an element $f$ in a regular local ring, we study matrix factorizations of $f$ with $d \ge 2$ factors, that is, we study tuples of square matrices $(\varphi_1,\varphi_2,\dots,\varphi_d)$ such that their product is $f$ times an identity…

Commutative Algebra · Mathematics 2021-02-16 Tim Tribone

Symmetric nonnegative matrix factorization has found abundant applications in various domains by providing a symmetric low-rank decomposition of nonnegative matrices. In this paper we propose a Frank-Wolfe (FW) solver to optimize the…

Machine Learning · Computer Science 2018-06-27 Han Zhao , Geoff Gordon

Solving evolutionary equations in a parallel-in-time manner is an attractive topic and many algorithms are proposed in recent two decades. The algorithm based on the block $\alpha$-circulant preconditioning technique has shown promising…

Numerical Analysis · Mathematics 2021-04-15 Shu-Lin Wu , Tao Zhou , Zhi Zhou

The so-called ``symplectic method'' is used for studying the linear stability of a self-gravitating collisionless stellar system, in which the particles are also submitted to an external potential. The system is steady and spherically…

Astrophysics · Physics 2015-06-24 J. Perez , J-J Aly

A second order accurate numerical scheme is proposed and implemented for the Landau-Lifshitz-Gilbert equation, which models magnetization dynamics in ferromagnetic materials, with large damping parameters. The main advantages of this method…

Computational Physics · Physics 2022-01-26 Yongyong Cai , Jingrun Chen , Cheng Wang , Changjian Xie

This paper considers a restriction to non-negative matrix factorization in which at least one matrix factor is stochastic. That is, the elements of the matrix factors are non-negative and the columns of one matrix factor sum to 1. This…

Machine Learning · Statistics 2016-09-20 Christopher Adams
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