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We present a formal analysis, in Isabelle/HOL, of optimisation algorithms for matroids, which are useful generalisations of combinatorial structures that occur in optimisation, and greedoids, which are a generalisation of matroids. Although…

Logic in Computer Science · Computer Science 2025-07-01 Mohammad Abdulaziz , Thomas Ammer , Shriya Meenakshisundaram , Adem Rimpapa

We present the general ideas on SuperSymmetric Quantum Mechanics (SUSY-QM) using different representations for the operators in question, which are defined by the corresponding bosonic Hamiltonian as part of SUSY Hamiltonian and its…

Quantum Physics · Physics 2019-02-06 J. Socorro , Marco A Reyes , Carlos Villaseñor Mora

We derive the self-energy functional theory for bosonic lattice systems with broken $U(1)$ symmetry by parametrizing the bosonic Baym-Kadanoff effective action in terms of one- and two-point self-energies. The formalism goes beyond other…

Quantum Gases · Physics 2016-11-14 Dario Hügel , Philipp Werner , Lode Pollet , Hugo U. R. Strand

The paper presents partial-realization theory and realization algorithms for linear switched systems. Linear switched systems are a particular subclass of hybrid systems. We formulate a notion of a partial realization and we present…

Optimization and Control · Mathematics 2010-10-26 Mihaly Petreczky , Jan H. van Schuppen

We show that any $n\times m$ matrix $A$ can be approximated in operator norm by a submatrix with a number of columns of order the stable rank of $A$. This improves on existing results by removing an extra logarithmic factor in the size of…

Functional Analysis · Mathematics 2018-07-19 Omer Friedland , Pierre Youssef

We perform an su(2) Hamiltonian reduction in the bosonic sector of the su(2)-invariant action for two free (4, 4, 0) supermultiplets. As a result, we get the five dimensional N=4 supersymmetric mechanics describing the motion of an isospin…

High Energy Physics - Theory · Physics 2015-05-19 Stefano Bellucci , Sergey Krivonos , Anton Sutulin

Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for…

Machine Learning · Computer Science 2017-07-28 Sanjar Karaev , Pauli Miettinen

Boson sampling is a key candidate for demonstrating quantum advantage, and has already yielded significant advances in quantum simulation, machine learning, and graph theory. In this work, a unification and extension of distinct forms of…

Quantum Physics · Physics 2025-12-29 Luca Bianchi , Carlo Marconi , Laura Ares , Davide Bacco , Jan Sperling

In Part 1 we study the spherical functions on compact symmetric pairs of arbitrary rank under a suitable multiplicity freeness assumption and additional conditions on the branching rules. The spherical functions are taking values in the…

Representation Theory · Mathematics 2017-06-08 Erik Koelink , Maarten van Pruijssen , Pablo Román

BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including…

Machine Learning · Computer Science 2024-02-07 Xiaoxing Wang , Jiaxing Li , Chao Xue , Wei Liu , Weifeng Liu , Xiaokang Yang , Junchi Yan , Dacheng Tao

In this work we present the results of several simulations on main-effect factorial designs. The goal of such simulations is to investigate the connections between the $D$-optimality of a design and its geometrical structure. By means of a…

Computation · Statistics 2016-04-18 Roberto Fontana , Fabio Rapallo

The partition function for a canonical ensemble of 2D Coulomb charges in a background potential (the Dyson gas) is realized as a vacuum expectation value of a group-like element constructed in terms of free fermionic operators. This…

Mathematical Physics · Physics 2011-02-03 A. Zabrodin

We present a neural network wavefunction framework for solving non-Abelian lattice gauge theories in a continuous group representation. Using a combination of $SU(2)$ equivariant neural networks alongside an $SU(2)$ invariant,…

High Energy Physics - Lattice · Physics 2025-09-17 Thomas Spriggs , Eliska Greplova , Juan Carrasquilla , Jannes Nys

Following the line of \cite{AS} we propose an improved algorithm which allows to calculate a D-dimensional fermion determinant integrating the exponent of D+1 dimensional Hermitean bosonic action. For a finite extra dimension the…

High Energy Physics - Lattice · Physics 2007-05-23 A. A. Slavnov

We study realisations of Lie (super)algebras in Weyl (super)algebras and connections with minimal representations. The main result is the construction of small realisations of Lie superalgebras, which we apply for two distinct purposes.…

Representation Theory · Mathematics 2017-07-20 Sigiswald Barbier , Kevin Coulembier

We consider algorithmic approaches to the D-optimality problem for cases where the input design matrix is large and highly structured, in particular implicitly specified as a full quadratic or linear response-surface model in several levels…

Optimization and Control · Mathematics 2023-09-11 Gabriel Ponte , Marcia Fampa , Jon Lee

In this work the matrix exponential function is solved analytically for the special orthogonal groups $SO(n)$ up to $n=9$. The number of occurring $k$-th matrix powers gets limited to $0\leq k \leq n-1$ by exploiting the Cayley-Hamilton…

Mathematical Physics · Physics 2023-08-29 Norbert Kaiser

We determine from 11D supergravity the quadratic bulk action for the physical bosonic fields relevant for the computation of correlation functions of normalized chiral operators in D=6, N=(0,2) and D=3, N=8 supersymmetric CFT in the large N…

High Energy Physics - Theory · Physics 2009-10-31 Fiorenzo Bastianelli , Roberto Zucchini

Several linear algebra routines for quantum computing use a basis of tensor products of identity and Pauli operators to describe linear operators, and obtaining the coordinates for any given linear operator from its matrix representation…

Quantum Physics · Physics 2020-11-19 Daniel Gunlycke , Mark C. Palenik , Alex R. Emmert , Sean A. Fischer

Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic Gaussian…

Machine Learning · Statistics 2019-05-10 Ali Hebbal , Loic Brevault , Mathieu Balesdent , El-Ghazali Talbi , Nouredine Melab