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In this paper, we give a geometric interpretation of optimal functionals in the context of intersection of symmetry planes and cyclic polytopes. For 1D CFTs, we demonstrate that at given derivative order, the functional is given by a…

High Energy Physics - Theory · Physics 2019-12-04 Yu-tin Huang , Wei Li , Guan-Lin Lin

Density functional theory (DFT) is an essential building block for modern theoretical physics, chemistry, and engineering, especially those concerning electronic properties. Through decades of development, various program packages for…

Materials Science · Physics 2022-11-21 Yusuke Nomura , Ryosuke Akashi

This work provides data-processing and majorization inequalities for $f$-divergences, and it considers some of their applications to coding problems. This work also provides tight bounds on the R\'{e}nyi entropy of a function of a discrete…

Information Theory · Computer Science 2021-04-01 Igal Sason

Several `edge-discovery' applications over graph-based data models are known to have worst-case quadratic time complexity in the nodes, even if the discovered edges are sparse. One example is the generic link discovery problem between two…

Artificial Intelligence · Computer Science 2017-07-04 Mayank Kejriwal

This paper studies the approximation of generalized ridge functions, namely of functions which are constant along some submanifolds of $\mathbb{R}^N$. We introduce the notion of linear-sleeve functions, whose function values only depend on…

Numerical Analysis · Mathematics 2017-01-26 Sandra Keiper

Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a DFNN. The…

Computation · Statistics 2018-05-28 Minh-Ngoc Tran , Nghia Nguyen , David Nott , Robert Kohn

Deep Matrix Factorization (DMF) is an emerging approach to the problem of matrix completion. Recent works have established that gradient descent applied to a DMF model induces an implicit regularization on the rank of the recovered matrix.…

Machine Learning · Computer Science 2021-06-08 Amit Boyarski , Sanketh Vedula , Alex Bronstein

This paper defines constrained functional similarity between 2-D trajectories via minimizing the H1 semi-norm of the difference between the trajectories. An exact general solution is obtained for the case wherein the components of the…

Optimization and Control · Mathematics 2016-08-31 Stefan M. Filipov , Ivan D. Gospodinov

An overview of the Conquest linear scaling density functional theory (DFT) code is given, focussing particularly on the scaling behaviour on modern high- performance computing (HPC) platforms. We demonstrate that essentially perfect linear…

Materials Science · Physics 2010-02-03 D. R. Bowler , T. Miyazaki

We establish an explicit link between depth-3 formulas and one-sided approximation by depth-2 formulas, which were previously studied independently. Specifically, we show that the minimum size of depth-3 formulas is (up to a factor of n)…

Computational Complexity · Computer Science 2017-05-11 Shuichi Hirahara

We consider the performance of the Depth First Search (DFS) algorithm on the random graph $G\left(n,\frac{1+\epsilon}{n}\right)$, $\epsilon>0$ a small constant. Recently, Enriquez, Faraud and M\'enard [2] proved that the stack $U$ of the…

Combinatorics · Mathematics 2022-07-27 Sahar Diskin , Michael Krivelevich

We define discrete generating series for arbitrary functions \( f \colon \mathbb{Z}^n \rightarrow \mathbb{C} \) and derive functional relations that these series satisfy. For linear difference equations with constant coefficients, we…

Classical Analysis and ODEs · Mathematics 2025-05-01 Vitaly Alekseev , Tom Cuchta , Alexander Lyapin

In this paper we demonstrate that the class of basic feasible functionals has recursion theoretic properties which naturally generalize the corresponding properties of the class of feasible functions. We also improve the Kapron - Cook…

Logic in Computer Science · Computer Science 2007-05-23 Aleksandar Ignjatovic , Arun Sharma

Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constraints. The exponential growth in the number of possible feature acquisition paths forces a…

Machine Learning · Computer Science 2026-05-13 Javier Fumanal-Idocin , Raquel Fernandez-Peralta , Javier Andreu-Perez

Generalized dualities had an intriguing incursion into Double Field Theory (DFT) in terms of local $O(d,d)$ transformations. We review this idea and use the higher derivative formulation of DFT to compute the first order corrections to…

High Energy Physics - Theory · Physics 2020-10-06 Tomas Codina , Diego Marques

The sparse-group lasso performs both variable and group selection, simultaneously using the strengths of the lasso and group lasso. It has found widespread use in genetics, a field that regularly involves the analysis of high-dimensional…

Machine Learning · Statistics 2025-09-18 Fabio Feser , Marina Evangelou

Recently, interesting empirical phenomena known as Neural Collapse have been observed during the final phase of training deep neural networks for classification tasks. We examine this issue when the feature dimension d is equal to the…

Machine Learning · Computer Science 2024-07-23 Yi Shen , Shao Gu

We introduce the semiclassical limit to electronic systems by taking the limit $\hbar\rightarrow 0$ in the solution of Schr\"odinger equations. We show that this limit is closely related to one type of strong correlation that is…

Computational Physics · Physics 2025-01-07 Yunzhi Li , Chen Li

We consider the family of CIFSs of generalized complex continued fractions with a complex parameter space. This is a new interesting example to which we can apply a general theory of infinite CIFSs and analytic families of infinite CIFSs.…

Dynamical Systems · Mathematics 2020-02-27 Kanji Inui , Hikaru Okada , Hiroki Sumi

We present a novel analysis of semidefinite programs (SDPs) with positive duality gaps, i.e. different optimal values in the primal and dual problems. These SDPs are extremely pathological, often unsolvable, and also serve as models of more…

Optimization and Control · Mathematics 2020-05-18 Gabor Pataki
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