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Random projection (RP) is a classical technique for reducing storage and computational costs. We analyze RP-based approximations of convex programs, in which the original optimization problem is approximated by the solution of a…

Information Theory · Computer Science 2014-04-30 Mert Pilanci , Martin J. Wainwright

This thesis develops a general theoretical and numerical framework for achieving high-contrast atom interferometry based on double Bragg diffraction (DBD). While DBD offers intrinsic symmetry, reduced sensitivity to internal-state…

Quantum Physics · Physics 2026-03-25 Rui Li

This article is concerned with the numerical solution of subspace optimization problems, consisting of minimizing a smooth functional over the set of orthogonal projectors of fixed rank. Such problems are encountered in particular in…

Numerical Analysis · Mathematics 2022-10-17 Eric Cancès , Gaspard Kemlin , Antoine Levitt

We report on progress in algorithms for iterative phase retrieval. The theory of convex optimization is used to develop and to gain insight into counterparts for the nonconvex problem of phase retrieval. We propose a relaxation of averaged…

Optimization and Control · Mathematics 2009-11-10 D. Russell Luke

This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only…

Information Theory · Computer Science 2013-07-22 Xi Liu , Osvaldo Simeone , Elza Erkip

The rate-distortion curve captures the fundamental tradeoff between compression length and resolution in lossy data compression. However, it conceals the underlying dynamics of optimal source encodings or test channels. We argue that these…

Information Theory · Computer Science 2023-07-03 Shlomi Agmon

Lossy compression algorithms are typically designed and analyzed through the lens of Shannon's rate-distortion theory, where the goal is to achieve the lowest possible distortion (e.g., low MSE or high SSIM) at any given bit rate. However,…

Machine Learning · Computer Science 2019-07-31 Yochai Blau , Tomer Michaeli

Series or orthogonal basis regression is one of the most popular non-parametric regression techniques in practice, obtained by regressing the response on features generated by evaluating the basis functions at observed covariate values. The…

Methodology · Statistics 2024-03-22 Yachong Yang , Arun Kumar Kuchibhotla , Eric Tchetgen Tchetgen

We study the compression of data in the case where the useful information is contained in a set rather than a vector, i.e., the ordering of the data points is irrelevant and the number of data points is unknown. Our analysis is based on…

Information Theory · Computer Science 2018-05-23 Günther Koliander , Dominic Schuhmacher , Franz Hlawatsch

In this article we present a modification of classical Radial Basis Function (RBF) interpolation techniques aimed at reducing oscillations near discontinuities in one and two dimensions. Our approach introduces an adaptive mechanism by…

Numerical Analysis · Mathematics 2026-03-25 José Kuruc , David Levin , Pep Mulet , Juan Ruiz-Álvarez , Dionisio F. Yáñez

Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this paper, we prove mathematically that distortion and…

Computer Vision and Pattern Recognition · Computer Science 2020-10-27 Yochai Blau , Tomer Michaeli

This paper presents a comprehensive experimental validation of a recently developed Ray Deflection Function (RDF) approach, which offers a new framework for modeling surface roughness effects in optical systems. Through detailed geometrical…

Optics · Physics 2025-05-05 Netzer Moriya

Signal degradation is ubiquitous and computational restoration of degraded signal has been investigated for many years. Recently, it is reported that the capability of signal restoration is fundamentally limited by the perception-distortion…

Information Theory · Computer Science 2020-06-30 Dong Liu , Haochen Zhang , Zhiwei Xiong

In this work, we present the convergence analysis of one-point large deviations rate functions (LDRFs) of the spatial finite difference method (FDM) for stochastic wave equations with small noise, which is essentially about the asymptotical…

Numerical Analysis · Mathematics 2022-09-20 Diancong Jin , Jialin Hong , Derui Sheng

For the HB problem with the CR constraint, the rate-distortion function is derived under the assumption that the side information sequences are (stochastically) degraded. The rate-distortion function is also calculated explicitly for three…

Information Theory · Computer Science 2016-11-18 Behzad Ahmadi , Ravi Tandon , Osvaldo Simeone , H. Vincent Poor

The constrained minimization (respectively maximization) of directed distances and of related generalized entropies is a fundamental task in information theory as well as in the adjacent fields of statistics, machine learning, artificial…

Information Theory · Computer Science 2024-10-28 Michel Broniatowski , Wolfgang Stummer

In this paper, we derive a Fast Reflected Forward-Backward (Fast RFB) algorithm to solve the problem of finding a zero of the sum of a maximally monotone operator and a monotone and Lipschitz continuous operator in a real Hilbert space. Our…

Optimization and Control · Mathematics 2025-10-20 Radu Ioan Bot , Dang-Khoa Nguyen , Chunxiang Zong

In this paper, we use reduced precision checking (RPC) to detect errors in floating point arithmetic. Prior work explored RPC for addition and multiplication. In this work, we extend RPC to a complete floating point unit (FPU), including…

Numerical Analysis · Computer Science 2015-10-06 Yaqi Zhang , Ralph Nathan , Daniel J. Sorin

An adaptive BDF2 implicit time-stepping method is analyzed for the phase field crystal model. The suggested method is proved to preserve a modified energy dissipation law at the discrete levels if the time-step ratios…

Numerical Analysis · Mathematics 2020-12-22 Hong-lin Liao , Bingquan Ji , Luming Zhang

Semi-supervised semantic segmentation allows model to mine effective supervision from unlabeled data to complement label-guided training. Recent research has primarily focused on consistency regularization techniques, exploring…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Xiaoyang Wang , Huihui Bai , Limin Yu , Yao Zhao , Jimin Xiao
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