Improvements and generalizations of Stochastic Knapsack and Markovian Bandits approximation algorithms
Year of publication: |
August 2018
|
---|---|
Authors: | Ma, Will |
Published in: |
Mathematics of operations research. - Catonsville, MD : INFORMS, ISSN 0364-765X, ZDB-ID 195683-8. - Vol. 43.2018, 3, p. 789-812
|
Subject: | approximation algorithms | stochastic knapsack | Markovian multi-armed bandit | stochastic programming | Stochastischer Prozess | Stochastic process | Markov-Kette | Markov chain | Theorie | Theory | Algorithmus | Algorithm | Mathematische Optimierung | Mathematical programming | Wahrscheinlichkeitsrechnung | Probability theory |
-
Penalty-based algorithms for the stochastic obstacle scene problem
Aksakalli, Vural, (2014)
-
Importance sampling in stochastic programming : a Markov chain Monte Carlo approach
Parpas, Panos, (2015)
-
Chance constrained unit commitment approximation under stochastic wind energy
Guo, Ge, (2021)
- More ...
-
Beyond IID : Data-Driven Decision-Making in Heterogeneous Environments
Besbes, Omar, (2022)
-
The Competitive Ratio of Threshold Policies for Online Unit-density Knapsack Problems
Ma, Will, (2019)
-
Inventory Balancing with Online Learning
Cheung, Wang Chi, (2018)
- More ...