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Showing 1 to 8 of 8 for “"Submodular optimization"”.
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Approximation algorithms for submodular optimization and graph problems
In this thesis, we consider combinatorial optimization problems involving submodular functions and graphs. The problems we study are NP-hard and therefore, assuming that P =/= NP, there do not exist polynomial-time algorithms that always output an optimal solution. In order to cope with the …
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Performance bounds for greedy strategies in submodular optimization problems
To view the abstract, please see the full text of the document.
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Submodular Optimization in Multi-Robot Teams: Robustness, Resilience, and Decentralization
… those problems are studied in the combinatorial optimization domain with the help of submodular and matroid optimization techniques. As a motivating example, we use a multi-robot environmental monitoring problem to extract the general formulation of a multi-robot decision-making problem. Consider …
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Distributionally Ambiguous Stackelberg Combinatorial Games for Submodular Optimization and Camera View-Frame Placement
… defender's recourse is a complex com- binatorial optimization problem and the attacker faces uncertainty and distributional am- biguity. We analyze these games through two complementary frameworks. Distributionally Robust Optimization (DRO) framework provides a risk-averse attacker with robust …
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Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination
… in the coordination achieved by means of submodular function optimization. Submodularity encodes the diminishing returns property that arises in multi-robot coordination. For example, the marginal gain of assigning an additional robot to track the same target diminishes as the number of …
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Combinatorial Optimization On Massive Datasets: Streaming, Distributed, And Massively Parallel Computation
… there is a rapidly growing need to solve various optimization tasks over such datasets. This in turn raises the following fundamental question: How well can we solve a large-scale optimization problem on massive datasets in a resource-efficient manner? The focus of this thesis is on answering this …
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A Cloud-Assisted Mobile Food Recognition System
… category using maximum cover in our proposed submodular optimization method. In the testing stage, we began by generating a set of candidate regions. For each region, a classification score was computed based on its extracted CNN features and the application predicted food names of the …
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Fair division: addressing complement-free valuations and online settings
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms