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We study differentially private (DP) algorithms for stochastic convex optimization: the problem of minimizing the population loss given i.i.d. samples from a distribution over convex loss functions. A recent work of Bassily et al. (2019)…

Machine Learning · Computer Science 2020-05-12 Vitaly Feldman , Tomer Koren , Kunal Talwar

We study the integer minimization of a quasiconvex polynomial with quasiconvex polynomial constraints. We propose a new algorithm that is an improvement upon the best known algorithm due to Heinz (Journal of Complexity, 2005). This…

Optimization and Control · Mathematics 2017-01-06 Robert Hildebrand , Matthias Köppe

We study the low rank regression problem $\my = M\mx + \epsilon$, where $\mx$ and $\my$ are $d_1$ and $d_2$ dimensional vectors respectively. We consider the extreme high-dimensional setting where the number of observations $n$ is less than…

Data Structures and Algorithms · Computer Science 2020-10-27 Qiong Wu , Felix Ming Fai Wong , Zhenming Liu , Yanhua Li , Varun Kanade

In this work, we initiate a thorough study of parameterized graph optimization problems in the distributed setting. In a parameterized problem, an algorithm decides whether a solution of size bounded by a \emph{parameter} $k$ exists and if…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-08-07 Ran Ben-Basat , Ken-ichi Kawarabayashi , Gregory Schwartzman

We present a deterministic near-linear time algorithm that computes the edge-connectivity and finds a minimum cut for a simple undirected unweighted graph G with n vertices and m edges. This is the first o(mn) time deterministic algorithm…

Data Structures and Algorithms · Computer Science 2018-10-30 Ken-ichi Kawarabayashi , Mikkel Thorup

We give an algorithm to compute a one-dimensional shape-constrained function that best fits given data in weighted-$L_{\infty}$ norm. We give a single algorithm that works for a variety of commonly studied shape constraints including…

Data Structures and Algorithms · Computer Science 2019-05-30 David Durfee , Yu Gao , Anup B. Rao , Sebastian Wild

Given independent standard Gaussian points $v_1, \ldots, v_n$ in dimension $d$, for what values of $(n, d)$ does there exist with high probability an origin-symmetric ellipsoid that simultaneously passes through all of the points? This…

Data Structures and Algorithms · Computer Science 2023-06-02 Aaron Potechin , Paxton Turner , Prayaag Venkat , Alexander S. Wein

One important question in the theory of lattices is to detect a shortest vector: given a norm and a lattice, what is the smallest norm attained by a non-zero vector contained in the lattice? We focus on the infinity norm and work with…

Optimization and Control · Mathematics 2026-03-18 Stefan Kuhlmann , Robert Weismantel

We consider the NP-hard problem of minimizing a convex quadratic function over the integer lattice ${\bf Z}^n$. We present a simple semidefinite programming (SDP) relaxation for obtaining a nontrivial lower bound on the optimal value of the…

Optimization and Control · Mathematics 2017-03-16 Jaehyun Park , Stephen Boyd

Coverings of convex bodies have emerged as a central component in the design of efficient solutions to approximation problems involving convex bodies. Intuitively, given a convex body $K$ and $\epsilon> 0$, a covering is a collection of…

Computational Geometry · Computer Science 2023-03-16 Sunil Arya , Guilherme D. da Fonseca , David M. Mount

The maximum volume $j$-simplex problem asks to compute the $j$-dimensional simplex of maximum volume inside the convex hull of a given set of $n$ points in $\mathbb{Q}^d$. We give a deterministic approximation algorithm for this problem…

Computational Geometry · Computer Science 2015-04-15 Aleksandar Nikolov

Finding sparse vectors is a fundamental problem that arises in several contexts including codes, subspaces, and lattices. In this work, we prove strong inapproximability results for all these variants using a novel approach that even…

Computational Complexity · Computer Science 2025-06-26 Vijay Bhattiprolu , Venkatesan Guruswami , Euiwoong Lee , Xuandi Ren

Matrix completion is the problem of recovering a low rank matrix by observing a small fraction of its entries. A series of recent works [KOM12,JNS13,HW14] have proposed fast non-convex optimization based iterative algorithms to solve this…

Numerical Analysis · Computer Science 2014-11-06 Prateek Jain , Praneeth Netrapalli

In the vertex connectivity survivable network design problem we are given an undirected graph G = (V,E) and connectivity requirement r(u,v) for each pair of vertices u,v. We are also given a cost function on the set of edges. Our goal is to…

Data Structures and Algorithms · Computer Science 2010-04-09 Pushkar Tripathi

Since the invention of the famous LLL algorithm, lattice reduction has been an extremely useful tool in computational number theory. By construction, the LLL algorithm deals with lattices living in a vector space endowed with a positive…

Computational Complexity · Computer Science 2025-11-21 Antoine Joux

We consider the planar Euclidean two-center problem in which given $n$ points in the plane we are to find two congruent disks of the smallest radius covering the points. We present a deterministic $O(n \log n)$-time algorithm for the case…

Computational Geometry · Computer Science 2020-07-09 Jongmin Choi , Hee-Kap Ahn

We study dynamic $(1+\epsilon)$-approximation algorithms for the all-pairs shortest paths problem in unweighted undirected $n$-node $m$-edge graphs under edge deletions. The fastest algorithm for this problem is a randomized algorithm with…

Data Structures and Algorithms · Computer Science 2018-03-02 Monika Henzinger , Sebastian Krinninger , Danupon Nanongkai

Given an $n$-vertex $m$-edge graph $G$ with non negative edge-weights, the girth of $G$ is the weight of a shortest cycle in $G$. For any graph $G$ with polynomially bounded integer weights, we present a deterministic algorithm that…

Data Structures and Algorithms · Computer Science 2018-10-25 Guillaume Ducoffe

We study the complexity of lattice problems in a world where algorithms, reductions, and protocols can run in superpolynomial time, revisiting four foundational results: two worst-case to average-case reductions and two protocols. We also…

We study the complexity of optimizing highly smooth convex functions. For a positive integer $p$, we want to find an $\epsilon$-approximate minimum of a convex function $f$, given oracle access to the function and its first $p$ derivatives,…

Optimization and Control · Mathematics 2021-12-06 Ankit Garg , Robin Kothari , Praneeth Netrapalli , Suhail Sherif
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