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We analyze the performance of the greedy algorithm, and also a discrete semi-gradient based algorithm, for maximizing the sum of a suBmodular and suPermodular (BP) function (both of which are non-negative monotone non-decreasing) under two…

Discrete Mathematics · Computer Science 2018-01-24 Wenruo Bai , Jeffrey A. Bilmes

This paper mainly addresses the optimization of $p$-th moment of $\mathbb{R}^n$-valued random variable. Through an ingenious approximation mechanism, one transforms the maximization problem into a sequence of minimization problems, which…

Optimization and Control · Mathematics 2016-07-26 Xiaojun Lu , Yanhua Wu

In this paper, we explore the two-point zeroth-order gradient estimator and identify the distribution of random perturbations that minimizes the estimator's asymptotic variance as the perturbation stepsize tends to zero. We formulate it as…

Machine Learning · Computer Science 2025-10-24 Shaocong Ma , Heng Huang

This paper investigates the value function, $V$, of a Mayer optimal control problem with the state equation given by a differential inclusion. First, we obtain an invariance property for the proximal and Fr\'echet subdifferentials of $V$…

Optimization and Control · Mathematics 2014-08-25 Piermarco Cannarsa , Hélène Frankowska , Teresa Scarinci

In this paper we investigate asymmetric forms of Doob maximal inequality. The asymmetry is imposed by noncommutativity. Let $(\M,\tau)$ be a noncommutative probability space equipped with a weak-$*$ dense filtration of von Neumann…

Operator Algebras · Mathematics 2016-05-04 Guixiang Hong , Marius Junge , Javier Parcet

A new primal-dual algorithm is presented for solving a class of non-convex minimization problems. This algorithm is based on canonical duality theory such that the original non-convex minimization problem is first reformulated as a…

Numerical Analysis · Computer Science 2013-01-01 Changzhi Wu , Chaojie Li , David Yang Gao

A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynomial optimization problems of noncommuting variables. Generating the relaxation, however, is a computationally demanding task, and only…

Mathematical Software · Computer Science 2015-06-15 Peter Wittek

We consider a system of two singularly perturbed Boundary Value Problems (BVPs) of convection-diffusion type with discontinuous source terms and a small positive parameter multiplying the highest derivatives. Then their solutions exhibit…

Numerical Analysis · Mathematics 2021-04-09 A. Ramesh Babu

We show improved fine-grained hardness of two key lattice problems in the $\ell_p$ norm: Bounded Distance Decoding to within an $\alpha$ factor of the minimum distance ($\mathrm{BDD}_{p, \alpha}$) and the (decisional) $\gamma$-approximate…

Computational Complexity · Computer Science 2025-07-30 Huck Bennett , Chris Peikert , Yi Tang

We prove existence and uniqueness of strong solutions for a class of second-order stochastic PDEs with multiplicative Wiener noise and drift of the form $\operatorname{div} \gamma(\nabla \cdot)$, where $\gamma$ is a maximal monotone graph…

Analysis of PDEs · Mathematics 2018-10-03 Carlo Marinelli , Luca Scarpa

This paper studies the disturbance decoupling problem by dynamic output feedback with required closed-loop stability, in the general case of nonstrictly-proper systems. We will show that the extension of the geometric solution based on the…

Dynamical Systems · Mathematics 2018-09-18 Fabrizio Padula , Lorenzo Ntogramatzidis

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of $(\epsilon,\delta)$-differential privacy (DP) in both Euclidean and non-Euclidean…

Machine Learning · Computer Science 2024-11-11 Raef Bassily , Cristóbal Guzmán , Michael Menart

While semidefinite programming (SDP) problems are polynomially solvable in theory, it is often difficult to solve large SDP instances in practice. One technique to address this issue is to relax the global positive-semidefiniteness (PSD)…

Optimization and Control · Mathematics 2020-02-11 Grigoriy Blekherman , Santanu S. Dey , Marco Molinaro , Shengding Sun

The Symmetric Primal-Dual Symplex Pivot Decision Strategy (spdspds) is a novel iterative algorithm to solve linear programming problems. A symplex pivoting operation is simply an exchange between a basic variable and a non-basic variable,…

Optimization and Control · Mathematics 2026-05-19 Keshava Prasad Halemane

We study the problem of super-resolution, where we recover the locations and weights of non-negative point sources from a few samples of their convolution with a Gaussian kernel. It has been recently shown that exact recovery is possible by…

Optimization and Control · Mathematics 2019-05-09 Stephane Chretien , Andrew Thompson , Bogdan Toader

We study the quantum corrections to the Fermi energy of a two-dimensional electron system, showing that it is attracted towards the Van Hove singularity for a certain range of doping levels. The scaling of the Fermi level allows to cure the…

Strongly Correlated Electrons · Physics 2009-11-07 J. Gonzalez

Recently a novel perturbative continuum limit for quantum gravity has been proposed and demonstrated to work at first order. Every interaction monomial $\sigma$ is dressed with a coefficient function $f^\sigma_\Lambda(\varphi)$ of the…

High Energy Physics - Theory · Physics 2021-04-21 Tim R. Morris

In this work we pursue the singular-vector analysis of the integrable perturbations of conformal theories that was initiated in hep-th/9603088. Here we consider the detailed study of the N=1 superconformal theory and show that all…

High Energy Physics - Theory · Physics 2009-10-30 Pierre Mathieu , Gerard Watts

Perturbation theory of vacuum spherically-symmetric spacetimes is a crucial tool to understand the dynamics of black hole perturbations. Spherical symmetry allows for an expansion of the perturbations in scalar, vector, and tensor…

General Relativity and Quantum Cosmology · Physics 2021-10-27 Michele Lenzi , Carlos F. Sopuerta

Submodularity is a discrete domain functional property that can be interpreted as mimicking the role of the well-known convexity/concavity properties in the continuous domain. Submodular functions exhibit strong structure that lead to…

Signal Processing · Electrical Eng. & Systems 2023-07-19 Ehsan Tohidi , Rouhollah Amiri , Mario Coutino , David Gesbert , Geert Leus , Amin Karbasi
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