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We present a local algorithm (constant-time distributed algorithm) for approximating max-min LPs. The objective is to maximise $\omega$ subject to $Ax \le 1$, $Cx \ge \omega 1$, and $x \ge 0$ for nonnegative matrices $A$ and $C$. The…

Distributed, Parallel, and Cluster Computing · Computer Science 2012-05-15 Patrik Floréen , Joel Kaasinen , Petteri Kaski , Jukka Suomela

We study the electrical distribution network reconfiguration problem, defined as follows. We are given an undirected graph with a root vertex, demand at each non-root vertex, and resistance on each edge. Then, we want to find a spanning…

Data Structures and Algorithms · Computer Science 2024-12-20 Takehiro Ito , Naonori Kakimura , Naoyuki Kamiyama , Yusuke Kobayashi , Yoshio Okamoto

This article is about both approximation theory and the numerical solution of partial differential equations (PDEs). First we introduce the notion of {\em reciprocal-log} or {\em log-lightning approximation} of analytic functions with…

Numerical Analysis · Mathematics 2020-10-06 Yuji Nakatsukasa , Lloyd N. Trefethen

We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative model (up to logarithmic factors), resolving an open…

Machine Learning · Computer Science 2024-11-05 Mark Rowland , Li Kevin Wenliang , Rémi Munos , Clare Lyle , Yunhao Tang , Will Dabney

Approximations of the Dirac delta distribution are commonly used to create sequences of smooth functions approximating nonsmooth (generalized) functions, via convolution. In this work, we show a priori rates of convergence of this…

Numerical Analysis · Mathematics 2021-11-18 Luca Heltai , Wenyu Lei

We study distributional off-policy evaluation (OPE), of which the goal is to learn the distribution of the return for a target policy using offline data generated by a different policy. The theoretical foundation of many existing work…

Machine Learning · Statistics 2025-03-13 Sungee Hong , Zhengling Qi , Raymond K. W. Wong

Stochastic approximation is a foundation for many algorithms found in machine learning and optimization. It is in general slow to converge: the mean square error vanishes as $O(n^{-1})$. A deterministic counterpart known as quasi-stochastic…

Optimization and Control · Mathematics 2024-03-26 Caio Kalil Lauand , Sean Meyn

The Dirac-{\delta} distribution may be realized through sequences of convlutions, the latter being also regarded as approximation to the identity. The present study proposes the so called pre-orthogonal adaptive Fourier decomposition…

Classical Analysis and ODEs · Mathematics 2020-08-04 Wei Qu , Tao Qian , Guan-Tie Deng

We investigate the energy per particle, static structure factor, and momentum distribution of the uniform electron gas for different conditions defined by the dimensionless temperature $\Theta = 0.25 - 1.0$ and average interparticle…

Materials Science · Physics 2025-06-13 Tommaso Morresi , Giovanni Garberoglio , Hongwei Xiong , Yunuo Xiong

While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal and Zhang (2022) recently proposed a variant that replaces risk with excess risk. Compared to DRO, the new…

Optimization and Control · Mathematics 2024-05-29 Lijun Zhang , Haomin Bai , Wei-Wei Tu , Ping Yang , Yao Hu

The low-frequency electric microfield distribution in a Coulomb plasma is calculated for various plasma parameters, from weak to strong Coulomb coupling and from zero to strong electron screening. Two methods of numerical calculations are…

Plasma Physics · Physics 2007-05-23 A. Y. Potekhin , G. Chabrier , D. Gilles

We present a time-optimal deterministic distributed algorithm for approximating a minimum weight vertex cover in hypergraphs of rank $f$. This problem is equivalent to the Minimum Weight Set Cover Problem in which the frequency of every…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-08-20 Ran Ben-Basat , Guy Even , Ken-ichi Kawarabayashi , Gregory Schwartzman

Electric power distribution systems will encounter fluctuations in supply due to the introduction of renewable sources with high variability in generation capacity. It is therefore necessary to provide algorithms that are capable of…

Optimization and Control · Mathematics 2018-05-07 Marie Maros , Joakim Jaldén

A rational expansion of the Fermi density operator is proposed. This approach allows to calculate efficiently physical properties of fermionic systems at finite temperatures without solving an eigenvalue problem. Using N evaluations of the…

Computational Physics · Physics 2009-10-30 F. Gagel

In recent times, orthogonal frequency-division multiplexing (OFDM)-based radar has gained wide acceptance given its applicability in joint radar-communication systems. However, realizing such a system on hardware poses a huge area and power…

Hardware Architecture · Computer Science 2024-10-15 Rajat Bhattacharjya , Alish Kanani , A Anil Kumar , Manoj Nambiar , M Girish Chandra , Rekha Singhal

The theoretical minimum emittance cells are the optimal configurations for achieving the absolute minimum emittance, if specific optics constraints are satisfied at the middle of the cell's dipole. Linear lattice design options based on an…

Accelerator Physics · Physics 2015-01-20 Fanouria Antoniou , Yannis Papaphilippou

We revisit the (block-angular) min-max resource sharing problem, which is a well-known generalization of fractional packing and the maximum concurrent flow problem. It consists of finding an $\ell_{\infty}$-minimal element in a Minkowski…

Data Structures and Algorithms · Computer Science 2021-11-15 Daniel Blankenburg

Experimental designs that are minimax in the presence of model misspecifications have been constructed so as to minimize the maximum, over classes of alternate response models, of the integrated mean squared error of the predicted values.…

Statistics Theory · Mathematics 2026-04-27 Rui Hu , Douglas P. Wiens

In this paper, we consider estimators for an additive functional of $\phi$, which is defined as $\theta(P;\phi)=\sum_{i=1}^k\phi(p_i)$, from $n$ i.i.d. random samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with alphabet size…

Information Theory · Computer Science 2017-12-08 Kazuto Fukuchi , Jun Sakuma

In this paper, we solve a set of hierarchy equations for the reduced statistical density operator in a grand canonical ensemble for an identical many-body fermion system without or with two-body interaction. We take the single-particle…

Statistical Mechanics · Physics 2009-11-10 Liqiang Wei , Chiachung Sun