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This paper studies first-order algorithms for solving fully composite optimization problems over convex and compact sets. We leverage the structure of the objective by handling its differentiable and non-differentiable components…

Optimization and Control · Mathematics 2023-07-13 Maria-Luiza Vladarean , Nikita Doikov , Martin Jaggi , Nicolas Flammarion

Based on the results of a recent reexamination of the quantization of systems with first-class and second-class constraints from the point of view of coherent-state phase-space path integration, we give additional examples of the…

Quantum Physics · Physics 2007-05-23 John R. Klauder

The demand for precision predictions in the field of high energy physics has dramatically increased over recent years. Experiments conducted at the LHC, as well as precision measurements at the intensity frontier such as Belle II require…

High Energy Physics - Phenomenology · Physics 2022-09-28 Marvin Gerlach , Florian Herren , Martin Lang

Model complexity in amplitude analyses is often a priori under-constrained since the underlying theory permits a large number of possible amplitudes to contribute to most physical processes. The use of an overly complex model results in…

Data Analysis, Statistics and Probability · Physics 2015-09-30 Baptiste Guegan , John Hardin , Justin Stevens , Mike Williams

A scheme for systematically achieving accurate numerical evaluation of multi-loop Feynman diagrams is developed. This shows the feasibility of a project aimed to produce a complete calculation for two-loop predictions in the Standard Model.…

High Energy Physics - Phenomenology · Physics 2008-11-26 G. Passarino

This dissertation addresses a topic that I have worked on over the past decade: the automation of next-to-leading order electroweak corrections in the Standard Model of particle physics. After introducing the basic concepts and techniques…

High Energy Physics - Phenomenology · Physics 2025-01-17 Hua-Sheng Shao

Deep Neural Networks have achieved remarkable success relying on the developing high computation capability of GPUs and large-scale datasets with increasing network depth and width in image recognition, object detection and many other…

Machine Learning · Computer Science 2020-01-08 E Zhenqian , Gao Weiguo

We describe a constructive procedure to separate overlapping infrared divergences in multi-loop integrals. Working with a parametric representation in D=4-2*epsilon dimensions, adequate subtractions lead to a Laurent series in epsilon,…

High Energy Physics - Phenomenology · Physics 2009-10-31 T. Binoth , G. Heinrich

Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better empirical…

Machine Learning · Computer Science 2018-07-25 Quanming Yao , James T. Kwok , Taifeng Wang , Tie-Yan Liu

The calculation of one loop integrals at finite temperature requires the evaluation of certain series, which converge very slowly or can even be divergent. Here we review a new method, recently devised by the author, for obtaining…

High Energy Physics - Theory · Physics 2007-05-23 Paolo Amore

We calculate 3-loop master integrals for heavy quark correlators and the 3-loop QCD corrections to the $\rho$-parameter. They obey non-factorizing differential equations of second order with more than three singularities, which cannot be…

High Energy Physics - Theory · Physics 2018-08-01 J. Ablinger , J. Blümlein , A. De Freitas , M. van Hoeij , E. Imamoglu , C. G. Raab , C. -S. Radu , C. Schneider

An algorithm for a family of self-starting high-order implicit time integration schemes with controllable numerical dissipation is proposed for both linear and nonlinear transient problems. This work builds on the previous works of the…

Numerical Analysis · Mathematics 2024-09-23 Daniel O'Shea , Xiaoran Zhang , Shayan Mohammadian , Chongmin Song

We consider the problem of soft gluon resummation for gauge theory amplitudes and cross sections, at next-to-eikonal order, using a Feynman diagram approach. At the amplitude level, we prove exponentiation for the set of factorizable…

High Energy Physics - Phenomenology · Physics 2015-03-17 Eric Laenen , Lorenzo Magnea , Gerben Stavenga , Chris D. White

We study realizable continual linear regression under random task orderings, a common setting for developing continual learning theory. In this setup, the worst-case expected loss after $k$ learning iterations admits a lower bound of…

Machine Learning · Computer Science 2025-10-28 Ran Levinstein , Amit Attia , Matan Schliserman , Uri Sherman , Tomer Koren , Daniel Soudry , Itay Evron

This work explores an unexpected application of Implicit Computational Complexity (ICC) to parallelize loops in imperative programs. Thanks to a lightweight dependency analysis, our algorithm allows splitting a loop into multiple loops that…

Programming Languages · Computer Science 2022-06-20 Clément Aubert , Thomas Rubiano , Neea Rusch , Thomas Seiller

One-loop amplitudes are to a large extent determined by their unitarity cuts in four dimensions. We show that the remaining rational terms can be obtained from the ultraviolet behaviour of the amplitude, and determine universal form factors…

High Energy Physics - Phenomenology · Physics 2010-10-27 T. Binoth , J. Ph. Guillet , G. Heinrich

In this paper, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics. An unknown discrete-time linear system evolves over time under Gaussian noise without…

Machine Learning · Computer Science 2019-03-22 Anastasios Tsiamis , George J. Pappas

The program package GoSam is presented which aims at the automated calculation of one-loop amplitudes for multi-particle processes. The amplitudes are generated in terms of Feynman diagrams and can be reduced using either D-dimensional…

High Energy Physics - Phenomenology · Physics 2015-06-03 G. Cullen , N. Greiner , G. Heinrich , G. Luisoni , P. Mastrolia , G. Ossola , T. Reiter , F. Tramontano

Stable concurrent learning and control of dynamical systems is the subject of adaptive control. Despite being an established field with many practical applications and a rich theory, much of the development in adaptive control for nonlinear…

Optimization and Control · Mathematics 2023-10-03 Nicholas M. Boffi , Jean-Jacques E. Slotine

We present the complete set of Feynman rules producing the rational terms of kind R_2 needed to perform any 1-loop calculation in the Electroweak Standard Model. Our formulae are given both in the R_xi gauge and in the Unitary gauge,…

High Energy Physics - Phenomenology · Physics 2011-01-17 M. V. Garzelli , I. Malamos , R. Pittau