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This paper focuses on regularisation methods using models up to the third order to search for up to second-order critical points of a finite-sum minimisation problem. The variant presented belongs to the framework of [3]: it employs random…

Numerical Analysis · Mathematics 2021-04-05 Stefania Bellavia , Gianmarco Gurioli , Benedetta Morini , Philippe L. Toint

The purpose of the present work is to expand substantially the type of control and estimation problems that can be addressed following the paradigm of Schr\"odinger bridges, by incorporating termination (killing) of stochastic flows.…

Optimization and Control · Mathematics 2024-06-24 Asmaa Eldesoukey , Olga Movilla Miangolarra , Tryphon T. Georgiou

In this paper, we propose new regularization, where integer-order differential operators are replaced by fractional-order operators. Regularization for quantum field theories based on application of the Riesz fractional derivatives of…

High Energy Physics - Theory · Physics 2018-05-23 Vasily E. Tarasov

Spin foams provide path integrals for quantum gravity, which employ discretizations as regulator. To obtain regulator independent predictions, we must remove these fiducial structures in a suitable refinement limit. In this chapter we…

General Relativity and Quantum Cosmology · Physics 2022-11-18 Seth K. Asante , Bianca Dittrich , Sebastian Steinhaus

With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification. The recently introduced deep structured state-space models (SSM), which…

Machine Learning · Computer Science 2024-03-25 Marco Forgione , Manas Mejari , Dario Piga

The noncommutativity of the momentum components, arising from spacetime torsion coupled to spin, replaces the integration over the momentum in loop Feynman diagrams with the summation over the momentum eigenvalues. This prescription…

High Energy Physics - Theory · Physics 2018-08-30 Nikodem Popławski

Regularization for optimization is a crucial technique to avoid overfitting in machine learning. In order to obtain the best performance, we usually train a model by tuning the regularization parameters. It becomes costly, however, when a…

Machine Learning · Computer Science 2020-08-18 Jingfeng Wu , Vladimir Braverman , Lin F. Yang

We propose and analyze a perturbative regularization method to approximate quadratic optimization problems with finite-dimensional degeneracy. The original problem is first approximated by a regularized problem depending on a small positive…

Numerical Analysis · Mathematics 2026-03-16 C. G. Gebhardt , I. Romero

Similarity Renormalization Group (SRG) flow equations can be used to unitarily soften nuclear Hamiltonians by decoupling high-energy intermediate state contributions to low-energy observables while maintaining the natural hierarchy of…

Nuclear Theory · Physics 2011-04-06 E. R. Anderson , S. K. Bogner , R. J. Furnstahl , R. J. Perry

This work represents a first systematic attempt to create a common ground for semi-classical and time-frequency analysis. These two different areas combined together provide interesting outcomes in terms of Schr\"odinger type equations.…

Mathematical Physics · Physics 2018-01-17 Elena Cordero , Maurice de Gosson , Fabio Nicola

Schwinger's quantization scheme is extended in order to solve the problem of the formulation of quantum mechanics on a space with a group structure. The importance of Killing vectors in a quantization scheme is showed. Usage of these…

High Energy Physics - Theory · Physics 2011-09-13 N. Chepilko , A. Romanenko

Algorithmic regularization uses a transformation of the equations of motion such that the leapfrog algorithm produces exact trajectories for two-body motion as well as regular results in numerical integration of the motion of strongly…

Astrophysics · Physics 2009-11-11 Seppo Mikkola , David Merritt

We investigate continuous regularization methods for linear inverse problems of static and dynamic type. These methods are based on dynamic programming approaches for linear quadratic optimal control problems. We prove regularization…

Optimization and Control · Mathematics 2021-01-27 S. Kindermann , A. Leitao

The role of dimensional regularization is discussed and compared with that of cut-off regularization in some quantum mechanical problems with ultraviolet divergence in two and three dimensions with special emphasis on the nucleon-nucleon…

Nuclear Theory · Physics 2009-10-31 Angsula Ghosh , Sadhan K. Adhikari , B. Talukdar

Some of the basic issues related to the regularization and anomalies in gauge theory are reviewed, with particular emphasis on the recent development in lattice gauge theory. The generalized Pauli-Villars regularization is discussed from a…

High Energy Physics - Lattice · Physics 2007-05-23 Kazuo Fujikawa

In a previous publication, we have constructed the Schr\"odinger functional in Wilson's lattice QCD. It was found that the naive continuum limit leads to a well-defined classical continuum theory. Starting from the latter, a formal…

High Energy Physics - Lattice · Physics 2009-10-28 Stefan Sint

We systematically examine various proposals which aim at increasing the accuracy in the determination of the renormalization of two-fermion lattice operators. We concentrate on three finite quantities which are particularly suitable for our…

High Energy Physics - Lattice · Physics 2011-09-13 M. Crisafulli , V. Lubicz , A. Vladikas

The method of higher covariant derivative regularization of gauge theories is reviewed. The objections raised in the literature last years are discussed and the consistency of the method is proven. New approach to regularization of…

High Energy Physics - Theory · Physics 2015-06-26 T. D. Bakeyev , A. A. Slavnov

We review our work on computations of the quantum corrections to the mass and the central charge of the susy kink. For the mass corrections, we find that the widely used momentum cut-off scheme gives an incorrect result, but we deduce…

High Energy Physics - Theory · Physics 2016-09-06 Alfred Scharff Goldhaber , Andrei Litvintsev , Peter van Nieuwenhuizen

A wide class of regularization problems in machine learning and statistics employ a regularization term which is obtained by composing a simple convex function \omega with a linear transformation. This setting includes Group Lasso methods,…

Machine Learning · Computer Science 2011-04-11 Andreas Argyriou , Charles A. Micchelli , Massimiliano Pontil , Lixin Shen , Yuesheng Xu