Randomized Strategies for Robust Combinatorial Optimization
نویسندگان
چکیده
منابع مشابه
Playing Stackelberg Opinion Optimization with Randomized Algorithms for Combinatorial Strategies
From a perspective of designing or engineering for opinion formation games in social networks, the opinion maximization (or minimization) problem has been studied mainly for designing subset selecting algorithms. We furthermore define a twoplayer zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the first-mover minimize the sum of expressed opini...
متن کاملRecoverable robust combinatorial optimization problems.dvi
This paper deals with two Recoverable Robust (RR) models for combinatorial optimization problems with uncertain costs. These models were originally proposed by Büsing (2012) for the shortest path problem with uncertain costs. In this paper, we generalize the RR models to a class of combinatorial optimization problems with uncertain costs and provide new positive and negative complexity results ...
متن کاملRandomized Parallel Algorithms for Combinatorial Optimization
In this paper we show some important randomization techniques for the parallel processing of discrete problems. In particular, we present several parallel randomized algorithms frequently used for sorting, packet routing, shortest paths problems, matching problems, depth rst search, minimum cost spanning trees, and maximal independent set problems. We also discuss the connection between randomi...
متن کاملMin-max-min robust combinatorial optimization
The idea of k-adaptability in two-stage robust optimization is to calculate a fixed number k of second-stage policies here-and-now. After the actual scenario is revealed, the best of these policies is selected. This idea leads to a min-max-min problem. In this paper, we consider the case where no first stage variables exist and propose to use this approach to solve combinatorial optimization pr...
متن کاملRobust Combinatorial Optimization with Exponential Scenarios
Following the well-studied two-stage optimization framework for stochastic optimization [14, 17], we study approximation algorithms for robust two-stage optimization problems with exponential number of scenarios. Prior to this work, Dhamdhere et al. [7] introduced approximation algorithms for two-stage robust optimization problems with polynomial number of scenarios. To model exponential number...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33017876