Zobrazeno 1 - 10
of 270
pro vyhledávání: '"Curtis Frank"'
To address unfairness issues in federated learning (FL), contemporary approaches typically use frequent model parameter updates and transmissions between the clients and server. In such a process, client-specific information (e.g., local dataset size
Externí odkaz:
http://arxiv.org/abs/2409.09532
An interior-point algorithm framework is proposed, analyzed, and tested for solving nonlinearly constrained continuous optimization problems. The main setting of interest is when the objective and constraint functions may be nonlinear and/or nonconve
Externí odkaz:
http://arxiv.org/abs/2408.16186
Stochastic sequential quadratic optimization (SQP) methods for solving continuous optimization problems with nonlinear equality constraints have attracted attention recently, such as for solving large-scale data-fitting problems subject to nonconvex
Externí odkaz:
http://arxiv.org/abs/2308.03687
A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is presented, analyzed, and demonstrated through experimental results. The algor
Externí odkaz:
http://arxiv.org/abs/2304.14907
A sequential quadratic optimization algorithm for minimizing an objective function defined by an expectation subject to nonlinear inequality and equality constraints is proposed, analyzed, and tested. The context of interest is when it is tractable t
Externí odkaz:
http://arxiv.org/abs/2302.14790
This paper introduces a new proximal stochastic gradient method with variance reduction and stabilization for minimizing the sum of a convex stochastic function and a group sparsity-inducing regularization function. Since the method may be viewed as
Externí odkaz:
http://arxiv.org/abs/2302.06790
Autor:
Yalcin, Gulcin Dinc, Curtis, Frank E.
Algorithms for solving nonconvex, nonsmooth, finite-sum optimization problems are proposed and tested. In particular, the algorithms are proposed and tested in the context of an optimization problem formulation arising in semi-supervised machine lear
Externí odkaz:
http://arxiv.org/abs/2207.09788
We propose a new inexact column-and-constraint generation (i-C&CG) method to solve two-stage robust optimization problems. The method allows solutions to the master problems to be inexact, which is desirable when solving large-scale and/or challengin
Externí odkaz:
http://arxiv.org/abs/2207.03291
Autor:
Aravena, Ignacio, Molzahn, Daniel K., Zhang, Shixuan, Petra, Cosmin G., Curtis, Frank E., Tu, Shenyinying, Wächter, Andreas, Wei, Ermin, Wong, Elizabeth, Gholami, Amin, Sun, Kaizhao, Sun, Xu Andy, Elbert, Stephen T., Holzer, Jesse T., Veeramany, Arun
The optimal power flow problem is central to many tasks in the design and operation of electric power grids. This problem seeks the minimum cost operating point for an electric power grid while satisfying both engineering requirements and physical la
Externí odkaz:
http://arxiv.org/abs/2206.07843
We propose combined allocation, assignment, sequencing, and scheduling problems under uncertainty involving multiple operation rooms (ORs), anesthesiologists, and surgeries, as well as methodologies for solving such problems. Specifically, given sets
Externí odkaz:
http://arxiv.org/abs/2204.11374