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DASH: Distributed Adaptive Sequencing Heuristic for Submodular Maximization [article]

Tonmoy Dey, Yixin Chen, Alan Kuhnle
2022 arXiv   pre-print
MapReduce (MR) model algorithms for maximizing monotone, submodular functions subject to a cardinality constraint (SMCC) are currently restricted to the use of the linear-adaptive (non-parallelizable)  ...  Our algorithms, R-DASH, T-DASH and G-DASH provide (0.316-ε), (0.375-ε) and (0.632-ε) approximation ratio respectively with near-optimal adaptive complexity.  ...  In this paper, we extend this analysis to propose distributed adaptive sequencing heuristics (DASH) using two design approaches. • First, using the DISTRIBUTED-FIRST-ADAPTIVE (DFA) approach we propose  ... 
arXiv:2206.09563v2 fatcat:6fx4ubgsp5ef5kfejmid6mo5tq

Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination [article]

Zirui Xu and Hongyu Zhou and Vasileios Tzoumas
2022 arXiv   pre-print
Such tasks are often modeled as submodular maximization coordination problems.  ...  Our algorithm generalizes the seminal Sequential Greedy algorithm by Fisher et al. to unpredictable environments, leveraging submodularity and algorithms for the problem of tracking the best expert.  ...  Then, at each time step t = 1, . . . , T , in sequence: • Each agent i draws an action a OSG i, t given the probability distribution p (i) t output by FSF | i (lines 4-7). • Each agent i stores {a OSG  ... 
arXiv:2209.12429v1 fatcat:ltzhvpuv3re4njrgygjunjuzji

Active Classification: Theory and Application to Underwater Inspection [article]

Geoffrey A. Hollinger, Urbashi Mitra, Gaurav S. Sukhatme
2011 arXiv   pre-print
We formally analyze the benefit of acting adaptively as new information becomes available.  ...  The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs.  ...  Further thanks to Hrdur Heidarsson at USC for assistance with data collection.  ... 
arXiv:1106.5829v1 fatcat:bktbuhsxmnh7rccgmb5jwjzw5e

The Team Surviving Orienteers problem: routing teams of robots in uncertain environments with survival constraints

Stefan Jorgensen, Robert H. Chen, Mark B. Milam, Marco Pavone
2017 Autonomous Robots  
We study the following multi-robot coordination problem: given a graph, where each edge is weighted by the probability of surviving while traversing it, find a set of paths for K robots that maximizes  ...  for the orienteering problem.  ...  Acknowledgements The authors would like to thank Federico Rossi, Edward Schmerling, and Sumeet Singh for their comments and insights which led to tighter analysis.  ... 
doi:10.1007/s10514-017-9694-1 fatcat:rswvul6ilvfubnib232bgnkmpi

Active Classification: Theory and Application to Underwater Inspection [chapter]

Geoffrey A. Hollinger, Urbashi Mitra, Gaurav S. Sukhatme
2016 Springer Tracts in Advanced Robotics  
We formally analyze the benefit of acting adaptively as new information becomes available.  ...  The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs.  ...  Acknowledgements The authors gratefully acknowledge Franz Hover and Brendan Englot at MIT for imaging sonar data and technical support while processing the data.  ... 
doi:10.1007/978-3-319-29363-9_6 fatcat:hdckjdl5hbbj3l4oef5so6qkva

Sensor Planning for Large Numbers of Robots [article]

Micah Corah
2021 arXiv   pre-print
One way to simplify planning for these tasks is to focus on maximizing sensing performance over a short time horizon.  ...  Specifically, consider the problem of how to select motions for a team of robots to maximize a notion of sensing quality (the sensing objective) over the near future, say by maximizing the amount of unknown  ...  However, CBBA can also converge more quickly in practice which is an important point for comparison. 3.6.5b Game theory for distributed submodular maximization Distributed submodular maximization has  ... 
arXiv:2102.04054v1 fatcat:zgg75lo6wfdwvekn5twqpmi7oe

The Team Surviving Orienteers Problem: Routing Robots in Uncertain Environments with Survival Constraints [article]

Stefan Jorgensen, Robert H. Chen, Mark B. Milam, Marco Pavone
2016 arXiv   pre-print
maximizes the expected number of nodes collectively visited, subject to constraints on the probability that each robot survives to its destination.  ...  threshold, and 1/λ< 1 is the approximation factor of an oracle routine for the well-known orienteering problem.  ...  ACKNOWLEDGEMENTS The authors would like to thank Federico Rossi and Edward Schmerling for their insights which led to tighter analysis.  ... 
arXiv:1612.03232v1 fatcat:5giu323tfrfhfmi5impc6uxn6e

Optimal thresholds for intrusion detection systems

Aron Laszka, Waseem Abbas, S. Shankar Sastry, Yevgeniy Vorobeychik, Xenofon Koutsoukos
2016 Proceedings of the Symposium and Bootcamp on the Science of Security - HotSos '16  
Thus, defenders have to strike the right balance between maximizing security and minimizing costs.  ...  intrusion detection systems is especially challenging in the case when multiple interdependent computer systems have to be defended against a strategic attacker, who can target computer systems in order to maximize  ...  Next, we introduce another linear-time greedy algorithm for finding an attack, adapted from [5] , in Algorithm 2.  ... 
doi:10.1145/2898375.2898399 dblp:conf/hotsos/LaszkaASVK16 fatcat:5cqob3crjzh4zez3bipoaqdq2u

Outbreak detection for temporal contact data

Martin Sterchi, Cristina Sarasua, Rolf Grütter, Abraham Bernstein
2021 Applied Network Science  
We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes.  ...  AbstractEpidemic spreading is a widely studied process due to its importance and possibly grave consequences for society.  ...  Acknowledgements We would like to thank Identitas AG for providing the pig movement data and Emily E. Raubach and the four anonymous reviewers for their valuable feedback.  ... 
doi:10.1007/s41109-021-00360-z pmid:33681456 pmcid:PMC7895791 fatcat:fmqgfyj5uve3ni5pin2mgcvr5u

Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM [article]

Yipu Zhao, Justin S. Smith, Patricio A. Vela
2020 arXiv   pre-print
To better suit BA-based VSLAM back-ends, the Good Graph predicts future estimation needs, dynamically assigns an appropriate size budget, and selects a condition-maximized subgraph for BA estimation.  ...  Inspired by recent progress in submodular submatrix selection [27] , [28] , we describe an efficient algorithm for selecting a subset of states to define a scale-limited local BA problem that maximizes  ...  The idea then led to the design of a cost-saving active feature matching algorithm based on maximizing a submodular objective with accuracy neutral outcomes [9] .  ... 
arXiv:2008.10123v1 fatcat:dlilgtwbw5b77jmfa4ppczjsam

Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks [article]

Nan Du, Yingyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha, Le Song
2017 arXiv   pre-print
Facing these user, monetary, and timing constraints, we formulate the problem as a submodular maximization task in a continuous-time diffusion model under the intersection of a matroid and multiple knapsack  ...  By exploiting the influence estimation algorithm as a subroutine, we develop an adaptive threshold greedy algorithm achieving an approximation factor k_a/(2+2 k) of the optimal when k_a out of the k knapsack  ...  We provide a novel formulation as a submodular maximization under an intersection of matroid constraints and group-knapsack constraints, and then design an efficient adaptive threshold greedy algorithm  ... 
arXiv:1612.02712v2 fatcat:odusb2zipzdcvoirvtgswtzsxy

Streaming Submodular Maximization with Fairness Constraints [article]

Yanhao Wang and Francesco Fabbri and Michael Mathioudakis
2020 arXiv   pre-print
We propose efficient algorithms for this fairness-aware variant of the streaming submodular maximization problem.  ...  In particular, we first provide a (1/2-ε)-approximation algorithm that requires O(1/ε·logk/ε) passes over the stream for any constant ε>0.  ...  ACKNOWLEDGMENTS We thank the anonymous reviewers for their helpful comments to improve this paper.  ... 
arXiv:2010.04412v1 fatcat:hzd5qcuwt5exzexb5lhxfnr27m

Towards Best Region Search for Data Exploration

Kaiyu Feng, Gao Cong, Sourav S. Bhowmick, Wen-Chih Peng, Chunyan Miao
2016 Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16  
of the spatial objects inside the region is maximized.  ...  Given a set O of spatial objects, a submodular monotone aggregate score function, and the size a × b of a query rectangle, the BRS problem aims to find a×b rectangular region such that the aggregate score  ...  We use solid line for the horizontal line passing the top edge of a rectangle and dashed for the bottom edge.  ... 
doi:10.1145/2882903.2882960 dblp:conf/sigmod/FengCBPM16 fatcat:utzlq4aejbhe7c5z267ywt56a4

MRF Energy Minimization and Beyond via Dual Decomposition

N Komodakis, N Paragios, G Tziritas
2011 IEEE Transactions on Pattern Analysis and Machine Intelligence  
For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, 3) and  ...  Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer  ...  We would like to thank the anonymous reviewers for their insightful and constructive comments that helped us to improve the clarity and presentation of the paper.  ... 
doi:10.1109/tpami.2010.108 pmid:20479493 fatcat:gpipcnkgdba7nj33it3ai63l7a

Efficient Approximation Algorithms for Adaptive Target Profit Maximization [article]

Keke Huang, Jing Tang, Xiaokui Xiao, Aixin Sun, Andrew Lim
2019 arXiv   pre-print
Given a social network G, the profit maximization (PM) problem asks for a set of seed nodes to maximize the profit, i.e., revenue of influence spread less the cost of seed selection.  ...  The target profit maximization (TPM) problem, which generalizes the PM problem, aims to select a subset of seed nodes from a target user set T to maximize the profit.  ...  sequence.  ... 
arXiv:1910.13073v1 fatcat:yn5rjkinq5ghhpsfeewff6brey
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