Zobrazeno 1 - 10
of 207
pro vyhledávání: '"Vavasis, Stephen A."'
Autor:
Hough, Matthew, Vavasis, Stephen A.
We present two first-order primal-dual algorithms for solving saddle point formulations of linear programs, namely FWLP (Frank-Wolfe Linear Programming) and FWLP-P. The former iteratively applies the Frank-Wolfe algorithm to both the primal and dual
Externí odkaz:
http://arxiv.org/abs/2402.18514
The $O(1/k^2)$ convergence rate in function value of accelerated gradient descent is optimal, but there are many modifications that have been used to speed up convergence in practice. Among these modifications are restarts, that is, starting the algo
Externí odkaz:
http://arxiv.org/abs/2310.07674
Primal-dual hybrid gradient (PDHG) is a first-order method for saddle-point problems and convex programming introduced by Chambolle and Pock. Recently, Applegate et al.\ analyzed the behavior of PDHG when applied to an infeasible or unbounded instanc
Externí odkaz:
http://arxiv.org/abs/2309.15009
We study the combination of proximal gradient descent with multigrid for solving a class of possibly nonsmooth strongly convex optimization problems. We propose a multigrid proximal gradient method called MGProx, which accelerates the proximal gradie
Externí odkaz:
http://arxiv.org/abs/2302.04077
We propose a clustering method that involves chaining four known techniques into a pipeline yielding an algorithm with stronger recovery guarantees than any of the four components separately. Given $n$ points in $\mathbb R^d$, the first component of
Externí odkaz:
http://arxiv.org/abs/2301.10901
We study several variants of decomposing a symmetric matrix into a sum of a low-rank positive semidefinite matrix and a diagonal matrix. Such decompositions have applications in factor analysis and they have been studied for many decades. On the one
Externí odkaz:
http://arxiv.org/abs/2209.05678
Autor:
Karimi, Sahar, Vavasis, Stephen
The method of nonlinear conjugate gradients (NCG) is widely used in practice for unconstrained optimization, but it satisfies weak complexity bounds at best when applied to smooth convex functions. In contrast, Nesterov's accelerated gradient (AG) me
Externí odkaz:
http://arxiv.org/abs/2111.11613
Autor:
Majmudar, Jimit, Vavasis, Stephen
Graph clustering problems typically aim to partition the graph nodes such that two nodes belong to the same partition set if and only if they are similar. Correlation Clustering is a graph clustering formulation which: (1) takes as input a signed gra
Externí odkaz:
http://arxiv.org/abs/2110.08385
Autor:
Jiang, Tao, Vavasis, Stephen
Sum-of-norms clustering is a clustering formulation based on convex optimization that automatically induces hierarchy. Multiple algorithms have been proposed to solve the optimization problem: subgradient descent by Hocking et al., ADMM and ADA by Ch
Externí odkaz:
http://arxiv.org/abs/2006.11355
Autor:
Majmudar, Jimit, Vavasis, Stephen
Community detection is a widely-studied unsupervised learning problem in which the task is to group similar entities together based on observed pairwise entity interactions. This problem has applications in diverse domains such as social network anal
Externí odkaz:
http://arxiv.org/abs/2004.07150