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We show how to construct highly symmetric algorithms for matrix multiplication. In particular, we consider algorithms which decompose the matrix multiplication tensor into a sum of rank-1 tensors, where the decomposition itself consists of…

Computational Complexity · Computer Science 2016-12-13 Joshua A. Grochow , Cristopher Moore

The atomic third-order open-shell many-body perturbation theory is developed. Special attention is paid to the generation and algebraic analysis of terms of the wave operator and the effective Hamiltonian as well. Making use of…

Atomic Physics · Physics 2015-05-19 R. Jursenas , G. Merkelis

Complex tables with multi-level headers, merged cells and heterogeneous layouts pose persistent challenges for LLMs in both understanding and reasoning. Existing approaches typically rely on table linearization or normalized grid modeling.…

Computation and Language · Computer Science 2026-02-03 Bin Cao , Huixian Lu , Chenwen Ma , Ting Wang , Ruizhe Li , Jing Fan

We consider a formulation of the algebraic Bethe ansatz for the six vertex model with non-diagonal open boundaries. Specifically, we study the case where both left and right $K$-matrices have an upper triangular form. We show that the main…

Mathematical Physics · Physics 2013-10-28 R. A. Pimenta , A. Lima-Santos

Tight binding (TB) models are one approach to the quantum mechanical many particle problem. An important role in TB models is played by hopping and overlap matrix elements between the orbitals on two atoms, which of course depend on the…

Other Condensed Matter · Physics 2009-11-11 Alin M. Elena , Matthias Meister

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over…

Machine Learning · Statistics 2018-04-04 Nicholas Lubbers , Justin S. Smith , Kipton Barros

We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation. For both the…

Machine Learning · Statistics 2018-09-05 Krzysztof Choromanski , Mark Rowland , Adrian Weller

We give a polynomial-time dynamic programming algorithm for solving the linear complementarity problem with tridiagonal or, more generally, Hessenberg P-matrices. We briefly review three known tractable matrix classes and show that none of…

Optimization and Control · Mathematics 2011-12-02 Bernd Gärtner , Markus Sprecher

Molecular dynamics simulation has been used to model pattern formation in three-dimensional Rayleigh--Benard convection at the discrete-particle level. Two examples are considered, one in which an almost perfect array of hexagonally-shaped…

Other Condensed Matter · Physics 2009-11-11 D. C. Rapaport

The equilibration of a trivalent polygonal network in two dimensions (2D) is a universal phenomenon in nature, but the underlying mathematical mechanism remains unclear. In this study, a relaxation algorithm based on a simple geometrical…

Biological Physics · Physics 2022-05-06 Kai Xu

We establish a new perturbation theory for orthogonal polynomials using a Riemann--Hilbert approach and consider applications in numerical linear algebra and random matrix theory. This new approach shows that the orthogonal polynomials with…

Probability · Mathematics 2022-09-23 Xiucai Ding , Thomas Trogdon

Special orthogonal matrices with rational elements form the group SO(n,Q), where Q is the field of rational numbers. A theorem describing the structure of an arbitrary matrix from this group is proved. This theorem yields an algorithm for…

Mathematical Software · Computer Science 2009-10-14 Ruslan Sharipov

A matroid is a machine capturing linearity of mathematical objects and producing combinatorial structures. Matroid structure arises everywhere since linearity is a ubiquitous concept. One natural way to obtain matroids is by considering…

Combinatorics · Mathematics 2023-03-14 Jaeho Shin

Rotation representations are foundational in fields such as computer graphics, robotics, and machine learning, where precise and efficient modeling of 3D orientations is critical. This paper comprehensively investigates diverse…

Graphics · Computer Science 2026-05-12 Aizierjiang Aiersilan , Haochen Liu , James Hahn

Lagrangian and Hamiltonian neural networks (LNNs and HNNs, respectively) encode strong inductive biases that allow them to outperform other models of physical systems significantly. However, these models have, thus far, mostly been limited…

Machine Learning · Computer Science 2022-11-14 Ravinder Bhattoo , Sayan Ranu , N. M. Anoop Krishnan

Real-world applications of computational fluid dynamics often involve the evaluation of quantities of interest for several distinct geometries that define the computational domain or are embedded inside it. For example, design optimization…

Numerical Analysis · Mathematics 2023-08-08 Guglielmo Padula , Francesco Romor , Giovanni Stabile , Gianluigi Rozza

This paper formulates optimal control problems for rigid bodies in a geometric manner and it presents computational procedures based on this geometric formulation for numerically solving these optimal control problems. The dynamics of each…

Optimization and Control · Mathematics 2008-05-07 Taeyoung Lee , Melvin Leok , N. Harris McClamroch

We prove that there exist uniform $(+,\times,/)$-circuits of size $O(n^3)$ to compute the basis generating polynomial of regular matroids on $n$ elements. By tropicalization, this implies that there exist uniform $(\max,+,-)$-circuits and…

Combinatorics · Mathematics 2025-11-05 Christoph Hertrich , Stefan Kober , Georg Loho

The real symmetric tridiagonal eigenproblem is of outstanding importance in numerical computations; it arises frequently as part of eigensolvers for standard and generalized dense Hermitian eigenproblems that are based on a reduction to…

Mathematical Software · Computer Science 2014-01-21 Matthias Petschow

We show that assuming the availability of the processor with variable precision arithmetic, we can compute matrix-by-matrix multiplications in $O(N^2log_2N)$ computational complexity. We replace the standard matrix-by-matrix multiplications…

Data Structures and Algorithms · Computer Science 2025-08-19 Maciej Paszyński