Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Shtern, Shimrit"'
Radiotherapy treatment planning is a challenging large-scale optimization problem plagued by uncertainty. Following the robust optimization methodology, we propose a novel, spatially based uncertainty set for robust modeling of radiotherapy planning,
Externí odkaz:
http://arxiv.org/abs/2402.17040
Autor:
Doron, Lior, Shtern, Shimrit
Simple bilevel problems are optimization problems in which we want to find an optimal solution to an inner problem that minimizes an outer objective function. Such problems appear in many machine learning and signal processing applications as a way t
Externí odkaz:
http://arxiv.org/abs/2212.09843
We propose a new homotopy-based conditional gradient method for solving convex optimization problems with a large number of simple conic constraints. Instances of this template naturally appear in semidefinite programming problems arising as convex r
Externí odkaz:
http://arxiv.org/abs/2207.03101
Publikováno v:
European Journal of Operational Research, 306(1), 83-104 (2023)
Real-life parallel machine scheduling problems can be characterized by: (i) limited information about the exact task duration at scheduling time, and (ii) an opportunity to reschedule the remaining tasks each time a task processing is completed and a
Externí odkaz:
http://arxiv.org/abs/2102.08677
Autor:
Postek, Krzysztof, Shtern, Shimrit
Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty. Two principal approaches for RO, the robust counterpart method and the adversarial approach, potentially lead to excessively large optimiz
Externí odkaz:
http://arxiv.org/abs/2101.02669
First-order methods for solving convex optimization problems have been at the forefront of mathematical optimization in the last 20 years. The rapid development of this important class of algorithms is motivated by the success stories reported in var
Externí odkaz:
http://arxiv.org/abs/2101.00935
We propose an algorithm for the Wireless Sensor Network localization problem, which is based on the well-known algorithmic framework of Alternating Minimization. We start with a non-smooth and non-convex minimization, and transform it into an equival
Externí odkaz:
http://arxiv.org/abs/2010.06959
Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in large scale optimization for machine learning and computational statistics. Numerous applications within these fields involve the
Externí odkaz:
http://arxiv.org/abs/2010.01009
Projection-free optimization via different variants of the Frank-Wolfe (FW), a.k.a. Conditional Gradient method has become one of the cornerstones in optimization for machine learning since in many cases the linear minimization oracle is much cheaper
Externí odkaz:
http://arxiv.org/abs/2002.04320
We investigate a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the type-$\infty$ Wasserstein ambiguity set. Our main result establishes th
Externí odkaz:
http://arxiv.org/abs/1907.07142