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Related papers: The Hybrid Bootstrap

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We apply the numerical bootstrap program to chiral operators in four-dimensional ${\mathcal N}=2$ SCFTs. In the first part of this work we study four-point functions in which all fields have the same conformal dimension. We give special…

High Energy Physics - Theory · Physics 2016-01-12 Madalena Lemos , Pedro Liendo

In this paper, we present a framework for the analytic bootstrap of three-point energy correlators, a crucial observable in $\mathcal{N}=4$ super Yang-Mills theory and quantum chromodynamics (QCD). Our approach combines spherical contour…

High Energy Physics - Phenomenology · Physics 2025-09-30 Jianyu Gong , Andrzej Pokraka , Kai Yan , Xiaoyuan Zhang

This article considers Hamiltonian mechanical systems with potential functions admitting jump discontinuities. The focus is on accurate and efficient numerical approximations of their solutions, which will be defined via the laws of…

Numerical Analysis · Mathematics 2022-01-05 Molei Tao , Shi Jin

The modern conformal bootstrap program often employs the method of linear functionals to derive the numerical or analytical bounds on the CFT data. These functionals must have a crucial "swapping" property, allowing to swap infinite…

High Energy Physics - Theory · Physics 2017-08-11 Jiaxin Qiao , Slava Rychkov

The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets which are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its…

Methodology · Statistics 2015-08-06 Srijan Sengupta , Stanislav Volgushev , Xiaofeng Shao

Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical forecasting approaches, such as the bottom-up and top-down…

Machine Learning · Computer Science 2020-06-04 Evangelos Spiliotis , Mahdi Abolghasemi , Rob J Hyndman , Fotios Petropoulos , Vassilios Assimakopoulos

Training a neural network (NN) typically relies on some type of curve-following method, such as gradient descent (GD) (and stochastic gradient descent (SGD)), ADADELTA, ADAM or limited memory algorithms. Convergence for these algorithms…

Machine Learning · Computer Science 2023-05-08 Michael A Kouritzin , Stephen Styles , Beatrice-Helen Vritsiou

Motivated by applications to critical phenomena and open theoretical questions, we study conformal field theories with $O(m)\times O(n)$ global symmetry in $d=3$ spacetime dimensions. We use both analytic and numerical bootstrap techniques.…

High Energy Physics - Theory · Physics 2020-12-10 Johan Henriksson , Stefanos R. Kousvos , Andreas Stergiou

The construction of the simultaneous confidence bands for the integrated hazard function is considered. The Nelson--Aalen estimator is used. The simultaneous confidence bands based on bootstrap methods are presented. Two methods of…

Statistics Theory · Mathematics 2007-06-13 Anna Dudek , Maciej Gocwin , Jacek Leskow

Control theoretical techniques have been successfully adopted as methods for self-adaptive systems design to provide formal guarantees about the effectiveness and robustness of adaptation mechanisms. However, the computational effort to…

Control architectures and autonomy stacks for complex engineering systems are often divided into layers to decompose a complex problem and solution into distinct, manageable sub-problems. To simplify designs, uncertainties are often ignored…

Optimization and Control · Mathematics 2021-12-30 Tyler Summers , Maryam Kamgarpour

We continue to develop Bootstrability -- a method merging Integrability and Conformal Bootstrap to extract CFT data in integrable conformal gauge theories such as $\mathcal{N}$=4 SYM. In this paper, we consider the 1D defect CFT defined on…

High Energy Physics - Theory · Physics 2022-06-08 Andrea Cavaglià , Nikolay Gromov , Julius Julius , Michelangelo Preti

Super learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results is by nested cross validation. It has been proposed by…

Machine Learning · Computer Science 2020-03-19 Krzysztof Mnich , Agnieszka Kitlas Golińska , Aneta Polewko-Klim , Witold R. Rudnicki

We demonstrate that substantial progress can be achieved in the study of the phase structure of 4-dimensional compact QED by a joint use of hybrid Monte Carlo and multicanonical algorithms, through an efficient parallel implementation. This…

High Energy Physics - Lattice · Physics 2016-08-25 G. Arnold , Th. Lippert , K. Schilling

In this paper, we attempt to explore the landscape of two-dimensional conformal field theories (2d CFTs) by efficiently searching for numerical solutions to the modular bootstrap equation using machine-learning-style optimization. The torus…

High Energy Physics - Theory · Physics 2026-05-05 Nathan Benjamin , A. Liam Fitzpatrick , Wei Li , Jesse Thaler

We use the conformal bootstrap approach to explore $5D$ CFTs with $O(N)$ global symmetry, which contain $N$ scalars $\phi_i$ transforming as $O(N)$ vector. Specifically, we study multiple four-point correlators of the leading $O(N)$ vector…

High Energy Physics - Theory · Physics 2017-05-24 Zhijin Li , Ning Su

Bootstrapping is often applied to get confidence limits for semiparametric inference of a target parameter in the presence of nuisance parameters. Bootstrapping with replacement can be computationally expensive and problematic when…

The Implicit Hitting Set (HS) approach has shown to be very effective for MaxSAT, Pseudo-boolean optimization and other boolean frameworks. Very recently, it has also shown its potential in the very similar Weighted CSP framework by means…

Artificial Intelligence · Computer Science 2025-01-15 Emma Rollón , Javier Larrosa , Aleksandra Petrova

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets---which are increasingly prevalent---the computation of bootstrap-based quantities can be prohibitively…

Methodology · Statistics 2012-06-29 Ariel Kleiner , Ameet Talwalkar , Purnamrita Sarkar , Michael I. Jordan

We describe a new algorithm, VEGAS+, for adaptive multidimensional Monte Carlo integration. The new algorithm adds a second adaptive strategy, adaptive stratified sampling, to the adaptive importance sampling that is the basis for its…

Computational Physics · Physics 2021-05-13 G. Peter Lepage