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Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
, 2023 Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been shown to obtain strong theoretical performance guarantees for a wide class ...
Chao Qian, Thomas Bäck, Aneta Neumann
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Submodular Optimization Problems and Greedy Strategies: A Survey [PDF]
Discrete Event Dynamic Systems: Theory and Applications, 2020 The greedy strategy is an approximation algorithm to solve optimization problems arising in decision making with multiple actions. How good is the greedy strategy compared to the optimal solution? In this survey, we mainly consider two classes of optimization problems where the objective function is submodular. The first is set submodular optimization,
Yajing Liu +2 more
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Risk-Sensitive Submodular Optimization
Proceedings of the AAAI Conference on Artificial Intelligence, 2018The conditional value at risk (CVaR) is a popular risk measure which enables risk-averse decision making under uncertainty. We consider maximizing the CVaR of a continuous submodular function, an extension of submodular set functions to a continuous domain.
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Temporal Biased Streaming Submodular Optimization
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021Submodular optimization lies at the core of many data mining and machine learning applications such as data summarization and subset selection. For data streams where elements arrive one at a time, streaming submodular optimization (SSO) algorithms are desired.
Junzhou Zhao +3 more
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Centralized Submodular Optimization
2015Submodularity enables efficient approximation of otherwise intractable set optimization problems using simply greedy or local search heuristics, making submodularity a valuable tool in a variety of applications. This chapter gives an overview of submodular optimization algorithms, with emphasis on centralized algorithms for maximizing submodular ...
Andrew Clark +3 more
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A Submodular Optimization Problem with Side Constraints
Mathematics of Operations Research, 1996In this paper we consider the general problem of optimizing over the intersection of a submodular base polyhedron and an affine space. An example is the following flow problem defined on a capacitated network: We wish to send a commodity from locations in a producing country to locations in a number of client countries so as to simultaneously maximize
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Submodular optimization for control of prosumer networks
2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2016We propose here a control based method for improving the storage placement in a prosumer network where generators and loads are stochastic. The particularity of our approach is to use the energy required for stabilizing the system as a criterion for the optimization of the storage placement.
Nicolas Gensollen +3 more
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Adaptive Robust Submodular Optimization and Beyond
2020Constrained submodular maximization has been extensively studied in the recent years. In this paper, we study adaptive robust optimization with nearly submodular structure (ARONSS). Our objective is to randomly select a subset of items that maximizes the worst case value of several reward functions simultaneously. Our work differs from existing studies
Shaojie Tang 0001, Jing Yuan 0002
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On linear, fractional, and submodular optimization
2023In this thesis, we study four fundamental problems in the theory of optimization. 1. In fractional optimization, we are interested in minimizing a ratio of two functions over some domain. A well-known technique for solving this problem is the Newton– Dinkelbach method.
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Submodular Optimization for Smooth Convergence
2015Smooth convergence ensures that the networked nodes converge to their desired states with minimal delay and error in their intermediate states. A submodular optimization approach to smooth convergence in networked systems is presented in this chapter. The approach is based on identifying connections between the system dynamics and the statistics of a ...
Andrew Clark +3 more
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