Zobrazeno 1 - 10
of 1 044
pro vyhledávání: '"68w40"'
This paper presents and analyzes the first matrix optimization model which allows general coordinate and spectral constraints. The breadth of problems our model covers is exemplified by a lengthy list of examples from the literature, including semide
Externí odkaz:
http://arxiv.org/abs/2410.09682
Autor:
Ortega, Tomas, Jafarkhani, Hamid
Recent advances in federated learning have shown that asynchronous variants can be faster and more scalable than their synchronous counterparts. However, their design does not include quantization, which is necessary in practice to deal with the comm
Externí odkaz:
http://arxiv.org/abs/2410.00242
Autor:
Alsubhi, Abdulmajeed, Renaut, Rosemary
We consider the solution of the $\ell_1$ regularized image deblurring problem using isotropic and anisotropic regularization implemented with the split Bregman algorithm. For large scale problems, we replace the system matrix $A$ using a Kronecker pr
Externí odkaz:
http://arxiv.org/abs/2410.00233
We introduce BRIDGET, a novel human-in-the-loop system for hybrid decision-making, aiding the user to label records from an un-labeled dataset, attempting to ``bridge the gap'' between the two most popular Hybrid Decision-Making paradigms: those feat
Externí odkaz:
http://arxiv.org/abs/2409.19415
Autor:
Roucairol, Milo, Cazenave, Tristan
We are interested in the automatic refutation of spectral graph theory conjectures. Most existing works address this problem either with the exhaustive generation of graphs with a limited size or with deep reinforcement learning. Exhaustive generatio
Externí odkaz:
http://arxiv.org/abs/2409.18626
Given a directed graph, the Minimal Feedback Arc Set (FAS) problem asks for a minimal set of arcs in a directed graph, which, when removed, results in an acyclic graph. Equivalently, the FAS problem asks to find an ordering of the vertices that minim
Externí odkaz:
http://arxiv.org/abs/2409.16443
Autor:
Toint, Philippe L.
The adaptive regularization algorithm for unconstrained nonconvex optimization was shown in Nesterov and Polyak (2006) and Cartis, Gould and Toint (2011) to require, under standard assumptions, at most $\mathcal{O}(\epsilon^{3/(3-q)})$ evaluations of
Externí odkaz:
http://arxiv.org/abs/2409.16047
Autor:
Buchbinder, Niv, Feldman, Moran
Maximization of submodular functions under various constraints is a fundamental problem that has been studied extensively. A powerful technique that has emerged and has been shown to be extremely effective for such problems is the following. First, a
Externí odkaz:
http://arxiv.org/abs/2409.14325
Autor:
Shirokoff, David, Zaleski, Philip
Stochastic gradient descent (SGD) is a popular algorithm for minimizing objective functions that arise in machine learning. For constant step-sized SGD, the iterates form a Markov chain on a general state space. Focusing on a class of separable (non-
Externí odkaz:
http://arxiv.org/abs/2409.12243
We consider an $\ell_1$-regularized inverse problem where both the forward and regularization operators have a Kronecker product structure. By leveraging this structure, a joint decomposition can be obtained using generalized singular value decomposi
Externí odkaz:
http://arxiv.org/abs/2409.00883