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Semidefinite programming is a fundamental problem class in convex optimization, but despite recent advances in solvers, solving large-scale semidefinite programs remains challenging. Generally the matrix functions involved are spectral or…

Optimization and Control · Mathematics 2025-11-04 Daniel Cederberg , Stephen Boyd

The true power of computational research typically can lay in either what it accomplishes or what it enables others to accomplish. In this work, both avenues are simultaneously embraced across several distinct efforts existing at three…

Materials Science · Physics 2024-11-06 Adam M. Krajewski

It is well-known that proper scaling can increase the efficiency of computational problems. In this paper we define and show that a balancing technique can substantially improve the computational efficiency of optimal control algorithms. We…

Optimization and Control · Mathematics 2018-10-29 I. M. Ross , Q. Gong , M. Karpenko , R. J. Proulx

Mathematical software systems are becoming more and more important in pure and applied mathematics in order to deal with the complexity and scalability issues inherent in mathematics. In the last decades we have seen a cambric explosion of…

Mathematical Software · Computer Science 2020-02-13 Katja Bercic , Jacques Carette , William M. Farmer , Michael Kohlhase , Dennis Müller , Florian Rabe , Yasmine Sharoda

These summer school lectures cover the use of algebraic techniques in various subfields of nuclear physics. After a brief description of groups and algebras, concepts of dynamical symmetry, dynamical supersymmetry, and supersymmetric…

Nuclear Theory · Physics 2015-05-20 A. B. Balantekin

The nature of dark matter, dark energy and large-scale gravity pose some of the most pressing questions in cosmology today. These fundamental questions require highly precise measurements, and a number of wide-field spectroscopic survey…

Instrumentation and Methods for Astrophysics · Physics 2016-02-05 B. Nord , A. Amara , A. Refregier , La. Gamper , Lu. Gamper , B. Hambrecht , C. Chang , J. E. Forero-Romero , S. Serrano , C. Cunha , O. Coles , A. Nicola , M. Busha , A. Bauer , W. Saunders , S. Jouvel , D. Kirk , R. Wechsler

Quantum many-body systems are challenging targets for computational physics due to their large degrees of freedom. The tensor networks, particularly Tensor Product States (TPS) and Projected Entangled Pair States (PEPS), effectively…

Strongly Correlated Electrons · Physics 2025-01-15 Yuichi Motoyama , Tsuyoshi Okubo , Kazuyoshi Yoshimi , Satoshi Morita , Tatsumi Aoyama , Takeo Kato , Naoki Kawashima

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and…

Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly…

Artificial Intelligence · Computer Science 2025-12-17 George Baryannis , Ilias Tachmazidis , Sotiris Batsakis , Grigoris Antoniou , Mario Alviano , Emmanuel Papadakis

A new topology optimization method called the Proportional Topology Optimization (PTO) is presented. As a non-gradient method, PTO is simple to understand, easy to implement, and is also efficient and accurate at the same time. It is…

Computational Engineering, Finance, and Science · Computer Science 2016-05-30 Emre Biyikli , Albert C. To

The gap equations lie at the core of the Bardeen-Cooper-Schrieffer (BCS) theory, a standard tool in the description of superfluidity. As a set of non-linear integral equations, the gap equations' inherent difficulties oftentimes hinder even…

Nuclear Theory · Physics 2025-10-10 Georgios Palkanoglou , Alexandros Gezerlis

Astronomy is entering in a new era of Extreme Intensive Data Computation and we have identified three major issues the new generation of projects have to face: Resource optimization, Heterogeneous Software Ecosystem and Data Transfer. We…

Instrumentation and Methods for Astrophysics · Physics 2012-12-11 Nicolas Kamennoff , Sébastien Foucaud , Sébastien Reybier

In recent years, several successful applications of the Artificial Neural Networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry, meteorology, and other fields of science. A major goal of…

The program package for the work with the Evaluated Nuclear Structure Data File is discussed. The program shell designed for the unification of the process of the evaluation of the nuclear data is proposed. This program shell may be used in…

Nuclear Experiment · Physics 2010-04-21 G. I. Shulyak , A. A. Rodionov

Recent work in deep learning has opened new possibilities for solving classical algorithmic tasks using end-to-end learned models. In this work, we investigate the fundamental task of solving linear systems, particularly those that are…

Machine Learning · Computer Science 2025-11-19 Pietro Sittoni , Francesco Tudisco

The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as $top$ $tagging$, is a crucial component in many measurements of Standard Model processes and searches for beyond the…

High Energy Physics - Experiment · Physics 2024-12-16 ATLAS Collaboration

The production at the LHC of boosted top quarks (top quarks with a transverse momentum that greatly exceeds their rest mass) is a promising process to search for phenomena beyond the Standard Model. In this contribution several examples are…

High Energy Physics - Experiment · Physics 2014-09-01 Elin Bergeaas Kuutmann

Optimization problems are pervasive across various sectors, from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers, as the…

Artificial Intelligence · Computer Science 2023-11-01 Ali AhmadiTeshnizi , Wenzhi Gao , Madeleine Udell

Probabilistic solvers for ordinary differential equations (ODEs) have emerged as an efficient framework for uncertainty quantification and inference on dynamical systems. In this work, we explain the mathematical assumptions and detailed…

Machine Learning · Statistics 2021-10-25 Nicholas Krämer , Nathanael Bosch , Jonathan Schmidt , Philipp Hennig
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