Zobrazeno 1 - 10
of 795
pro vyhledávání: '"Røyset, A."'
Autor:
Royset, Johannes O.
Variational analysis provides the theoretical foundations and practical tools for constructing optimization algorithms without being restricted to smooth or convex problems. We survey the central concepts in the context of a concrete but broadly appl
Externí odkaz:
http://arxiv.org/abs/2411.04317
Autor:
Royset, Johannes O.
Solutions of bilevel optimization problems tend to suffer from instability under changes to problem data. In the optimistic setting, we construct a lifted, alternative formulation that exhibits desirable stability properties under mild assumptions th
Externí odkaz:
http://arxiv.org/abs/2408.13323
This work proposes an implementable proximal-type method for a broad class of optimization problems involving nonsmooth and nonconvex objective and constraint functions. In contrast to existing methods that rely on an ad hoc model approximating the n
Externí odkaz:
http://arxiv.org/abs/2407.07471
Labeling errors in datasets are common, if not systematic, in practice. They naturally arise in a variety of contexts-human labeling, noisy labeling, and weak labeling (i.e., image classification), for example. This presents a persistent and pervasiv
Externí odkaz:
http://arxiv.org/abs/2405.20531
Stochastic optimization problems are generally known to be ill-conditioned to the form of the underlying uncertainty. A framework is introduced for optimal control problems with partial differential equations as constraints that is robust to inaccura
Externí odkaz:
http://arxiv.org/abs/2405.00176
Autor:
Deride, Julio, Royset, Johannes O.
Solutions of an optimization problem are sensitive to changes caused by approximations or parametric perturbations, especially in the nonconvex setting. This paper investigates the ability of substitute problems, constructed from Rockafellian functio
Externí odkaz:
http://arxiv.org/abs/2404.18097
For parameterized mixed-binary optimization problems, we construct local decision rules that prescribe near-optimal courses of action across a set of parameter values. The decision rules stem from solving risk-adaptive training problems over classes
Externí odkaz:
http://arxiv.org/abs/2310.09844
In many recent works, there is an increased focus on designing algorithms that seek flatter optima for neural network loss optimization as there is empirical evidence that it leads to better generalization performance in many datasets. In this work,
Externí odkaz:
http://arxiv.org/abs/2310.00488
In multi-agent search planning for a randomly moving and camouflaging target, we examine heterogeneous searchers that differ in terms of their endurance level, travel speed, and detection ability. This leads to a convex mixed-integer nonlinear progra
Externí odkaz:
http://arxiv.org/abs/2309.02629
Autor:
Wenche Sjursen, Hanne K. Hyldebrandt, Liss Anne S. Lavik, Bjørn Ivar Haukanes, Sarah Ariansen, Siri Briskemyr, Anna E. Sylvander, Marianne T. Haavind, Maren F. Olsen, Elin S. Røyset, Hildegunn Vetti, Astrid Stormorken, Eli Marie Grindedal
Publikováno v:
Hereditary Cancer in Clinical Practice, Vol 22, Iss 1, Pp 1-8 (2024)
Abstract Background In Norway, we have offered testing of PMS2 since 2006, and have a large national cohort of carriers. The aim of this study was to describe all PMS2 variants identified, and to describe frequency, spectrum and penetrance of cancers
Externí odkaz:
https://doaj.org/article/d5372f9d9f07459ca2681dd5b204ddce
