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Submodular Optimization with Applications to Decision and Control

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Pith's one-line read Submodular set functions unify subset-selection problems in decision and control, enabling greedy algorithms to deliver constant-factor approximation guarantees for monotone objectives.

desk verdict This is a competent survey that collects existing submodular results for control but introduces no new theorems or algorithms. read the letter →

arxiv 2606.10192 v1 pith:SGDD3LIK submitted 2026-06-08 math.OC cs.SYeess.SY

Shamak Dutta , Bahman Gharesifard , Stephen L. Smith This is my paper
classification math.OCcs.SYeess.SY
keywords submodularoptimizationgreedyalgorithmssensorschedulingcontroltheoryapproximationmatroidsinformativepathplanningcontrollabilityGramian
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper surveys how submodular functions, defined by diminishing returns, supply a combinatorial structure for selecting subsets in control tasks such as sensor placement and path planning. It shows that this structure permits simple greedy procedures to reach guaranteed performance levels under common constraints like matroids and knapsacks, with randomized extensions covering non-monotone cases. The survey compiles structural refinements such as curvature bounds, lists concrete applications across multi-agent systems and resource allocation, and identifies which standard control functionals satisfy submodularity exactly. A reader would care because the guarantees translate directly to implementable algorithms for otherwise NP-hard selection problems while clarifying which objectives inherit the theory and which do not.

What carries the argument

Submodular set functions with the diminishing-returns property, together with greedy algorithms under matroid, knapsack and p-system constraints.

What would settle it

A concrete control objective whose value cannot be bounded away from the submodular case by any fixed curvature or submodularity ratio, causing the stated approximation factors to fail on instances drawn from that objective.

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Extended reading notes

Core claim

Submodular set functions, characterized by the diminishing-returns property, provide a unifying combinatorial framework for many subset-selection problems in decision and control. Although exact maximization is NP-hard in general, the structural properties enable simple greedy algorithms that achieve constant-factor approximation guarantees for monotone objectives, with randomized greedy-based variants extending such guarantees to the non-monotone case. The survey covers curvature and the submodularity ratio, constraint families including matroids and p-systems, main approximation algorithms with current ratios, and applications in sensor scheduling, leader-follower systems, informative path

Load-bearing premise

The objectives arising in the listed control applications are either exactly submodular or can be usefully bounded via curvature and submodularity ratio so that the approximation results apply directly.

Editorial extensions

If this is right

  • Greedy selection yields constant-factor performance for sensor scheduling under cardinality or matroid constraints.
  • Leader-follower coordination and multi-agent resource allocation inherit the same approximation guarantees when formulated as submodular maximization.
  • Informative path planning admits randomized greedy procedures with guarantees when the objective is monotone or non-monotone submodular.
  • Log-determinant and rank functionals of the controllability Gramian can be maximized directly with the surveyed algorithms, while steady-state Kalman covariance cannot.
  • Distributed and robust variants of the algorithms extend to networked and uncertain settings with instance-dependent refinements.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same diminishing-returns lens could be tested on objectives from reinforcement learning that select informative state subsets.
  • Instance-specific curvature values measured on real plants would tighten the practical performance gap between worst-case ratios and observed behavior.
  • Game-theoretic equilibria in submodular resource games may inherit stability properties from the underlying greedy dynamics.
  • Open directions listed in the survey suggest checking whether average control energy can be approximated by nearby submodular surrogates.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

0 major / 2 minor

Summary. The manuscript is a survey on submodular optimization with applications to decision and control. It reviews structural properties of submodular functions (including curvature and submodularity ratio), constraint families such as matroids and knapsacks, approximation algorithms for monotone and non-monotone maximization with current ratios and hardness results, and applications across sensor scheduling, multi-agent coordination, leader-follower systems, distributed optimization, game theory, resource allocation, and informative path planning. It emphasizes greedy-based methods and instance-dependent refinements, and closes with observations on the submodularity status of specific control objectives (log-det and rank of controllability Gramian are submodular; steady-state Kalman error covariance and inverse-Gramian energy are not) along with open directions.

Significance. As a synthesis of established theory and applications, the survey can serve as a useful reference for the systems and control community by unifying subset-selection problems under the submodular framework and highlighting practically implementable algorithms. Credit is due for collecting and contrasting the submodularity status of canonical control functionals drawn from the literature, and for identifying cross-cutting open problems.

minor comments (2)
  1. The abstract and closing section state that certain functionals are submodular while others are not; a compact summary table listing the status for each mentioned control objective would improve readability.
  2. Ensure that the cited approximation ratios and hardness results in the algorithms section reflect the most recent literature available at the time of submission.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript and for recommending acceptance. The report contains no major comments requiring response.

Circularity Check

0 steps flagged · score 0.0 of 10

Survey paper with no internal derivations or self-referential reductions

full rationale

This is a review synthesizing established theory and applications of submodular optimization. No new proofs, algorithms, fitted parameters, or empirical results are presented. All approximation guarantees, structural properties, and application observations are attributed to prior literature. The closing remarks on which control objectives are submodular are presented as observations drawn from existing work, with no equations or claims that reduce to the paper's own inputs by construction. No load-bearing self-citations or ansatzes are introduced.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This is a survey paper; no new free parameters, axioms, or invented entities are introduced by the authors.

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Cite this review

Pith. "Pith review of Submodular Optimization with Applications to Decision and Control." pith.science (2026). https://pith.science/paper/SGDD3LIK

@misc{pith2026260610192,
  author       = {Pith},
  title        = {Pith review of: Submodular Optimization with Applications to Decision and Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SGDD3LIK}},
  note         = {Machine review of arXiv:2606.10192}
}
abstract

Submodular set functions, characterized by the diminishing-returns property, provide a unifying combinatorial framework for many subset-selection problems in decision and control. Although exact maximization is NP-hard in general, the structural properties of submodular functions enable simple greedy algorithms that achieve constant-factor approximation guarantees for monotone objectives, with randomized greedy-based variants extending such guarantees to the non-monotone case. This survey reviews the theory, algorithms, and applications of submodular optimization with a focus on systems and control. We cover the structural properties of submodular functions, including curvature and the submodularity ratio, the constraint families that arise in practice (matroids, knapsack, and $p$-systems), and the main approximation algorithms for monotone and non-monotone submodular maximization, with up-to-date approximation ratios and hardness results. We then survey applications across sensor scheduling, multi-agent coordination, robust submodular optimization, leader-follower systems, distributed submodular optimization, game theory, system theory, resource allocation, social networks, and informative path planning. The survey emphasizes practically implementable greedy-based algorithms and instance-dependent refinements via curvature and the submodularity ratio. We close with observations on canonical control-theoretic objectives: certain functionals are submodular (the log-determinant and rank of the controllability Gramian, and the log-determinant of the Kalman filter information matrix), whereas closely related objectives fail to be sub- or supermodular (the steady-state Kalman filter error covariance, and the average control energy obtained from the inverse Gramian). We also highlight the cross-cutting open directions that follow.

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Works this paper leans on

300 extracted references · 4 canonical work pages

  1. [1]

    Abou Rahal, G

    J. Abou Rahal, G. de Veciana, T. Shimizu, and H. Lu. Optimizing timely coverage in communication-constrained collaborative sensing systems. IEEE Transactions on Control of Network Systems, 11 0 (3): 0 1717--1729, 2024

    2024

  2. [2]

    Adiprasito, J

    K. Adiprasito, J. Huh, and E. Katz. Hodge theory for combinatorial geometries. Annals of Mathematics, 188 0 (2): 0 381--452, 2018

    2018

  3. [3]

    Akbarzadeh and A

    N. Akbarzadeh and A. Mahajan. Restless bandits with controlled restarts: Indexability and computation of W hittle index. In IEEE Conf.\ on Decision and Control , pages 7294--7300, 2019

    2019

  4. [4]

    Akcin, O

    O. Akcin, O. Unuvar, O. Ure, and S. P. Chinchali. Fleet active learning: A submodular maximization approach. In Proceedings of the 7th Conference on Robot Learning, volume 229, pages 1378--1399, 2023

    2023

  5. [5]

    Albert and R

    A. Albert and R. Rajagopal. Strategic scheduling of residential energy consumers. In IEEE Conf.\ on Decision and Control , pages 3260--3265, 2015

    2015

  6. [6]

    Anevlavis, J

    T. Anevlavis, J. Bunton, A. Parayil, J. George, and P. Tabuada. To beam or not to beam? beamforming with submodularity-inspired group sparsity. In IEEE Conf.\ on Decision and Control , pages 390--395, 2020

    2020

  7. [7]

    Arora, S

    S. Arora, S. Choudhury, D. Althoff, and S. Scherer. Emergency maneuver library-ensuring safe navigation in partially known environments. In IEEE Int. Conf.\ on Robotics and Automation , pages 6431--6438, 2015

    2015

  8. [8]

    F. Bach. Submodular functions: from discrete to continuous domains. Mathematical Programming, 175: 0 419--459, 2019

    2019

Show all 300 references
  1. [9]

    Bach et al

    F. Bach et al. Learning with submodular functions: A convex optimization perspective. Foundations and Trends in Machine Learning , 6 0 (2-3): 0 145--373, 2013

    2013

  2. [10]

    Badanidiyuru and J

    A. Badanidiyuru and J. Vondr \'a k. Fast algorithms for maximizing submodular functions. In Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms, pages 1497--1514. SIAM, 2014

    2014

  3. [11]

    Badanidiyuru, B

    A. Badanidiyuru, B. Mirzasoleiman, A. Karbasi, and A. Krause. Streaming submodular maximization: Massive data summarization on the fly. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 671--680, 2014

    2014

  4. [12]

    Baggio and R

    A. Baggio and R. K. Kalaimani. Optimal pinning control for synchronization over temporal networks. In A merican C ontrol C onference , pages 3746--3751, 2024

    2024

  5. [13]

    Balasubramanian, L

    R. Balasubramanian, L. Zhou, P. Tokekar, and P. Sujit. Risk-aware submodular optimization for stochastic travelling salesperson problem. In IEEE/RSJ Int.\ Conf.\ on Intelligent Robots & Systems, pages 4720--4725, 2021

    2021

  6. [14]

    A. S. Bedi, D. Peddireddy, V. Aggarwal, and A. Koppel. Efficient large-scale gaussian process bandits by believing only informative actions. In Learning for Dynamics and Control, pages 924--934. PMLR, 2020

    2020

  7. [15]

    Bhargav, M

    J. Bhargav, M. Ghasemi, and S. Sundaram. On the complexity and approximability of optimal sensor selection for mixed-observable markov decision processes. In 2023 American Control Conference (ACC), pages 3332--3337, 2023

    2023

  8. [16]

    A. Bian, K. Levy, A. Krause, and J. M. Buhmann. Continuous DR -submodular maximization: Structure and algorithms. Advances in Neural Information Processing Systems, 30, 2017 a

    2017

  9. [17]

    A. A. Bian, B. Mirzasoleiman, J. Buhmann, and A. Krause. Guaranteed non-convex optimization: Submodular maximization over continuous domains. In Artificial Intelligence and Statistics, pages 111--120, 2017 b

    2017

  10. [18]

    J. Bilmes. Submodularity in machine learning and artificial intelligence. arXiv preprint arXiv:2202.00132, 2022

    2022

  11. [19]

    M. Bini, P. Frasca, C. Ravazzi, and F. Dabbene. Graph structure-based heuristics for optimal targeting in social networks. IEEE Transactions on Control of Network Systems, 9 0 (3): 0 1189--1201, 2022

    2022

  12. [20]

    Binney, A

    J. Binney, A. Krause, and G. S. Sukhatme. Optimizing waypoints for monitoring spatiotemporal phenomena. The International Journal of Robotics Research, 32 0 (8): 0 873--888, 2013

    2013

  13. [21]

    B y k, D

    E. B y k, D. P. Losey, M. Palan, N. C. Landolfi, G. Shevchuk, and D. Sadigh. Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences. The International Journal of Robotics Research, 41 0 (1): 0 45--67, 2022

    2022

  14. [22]

    Bogunovic, S

    I. Bogunovic, S. Mitrovi \'c , J. Scarlett, and V. Cevher. Robust submodular maximization: A non-uniform partitioning approach. In International Conference on Machine Learning, pages 508--516, 2017

    2017

  15. [23]

    S. D. Bopardikar. A randomized approach to sensor placement with observability assurance. Automatica, 123: 0 109340, 2021

    2021

  16. [24]

    P. N. Brown and J. R. Marden. On the feasibility of local utility redesign for multiagent optimization. In E uropean C ontrol C onference , pages 3396--3401, 2019

    2019

  17. [25]

    Bucciarelli, S

    M. Bucciarelli, S. Paoletti, E. Dall’Anese, and A. Vicino. On the greedy placement of energy storage systems in distribution grids. In A merican C ontrol C onference , pages 1280--1285, 2020

    2020

  18. [26]

    Buchbinder and M

    N. Buchbinder and M. Feldman. Constrained submodular maximization via new bounds for DR -submodular functions. In Proceedings of the 56th Annual ACM Symposium on Theory of Computing, pages 1820--1831, 2024

    2024

  19. [27]

    Buchbinder, M

    N. Buchbinder, M. Feldman, J. Naor, and R. Schwartz. Submodular maximization with cardinality constraints. In Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms, pages 1433--1452. SIAM, 2014

    2014

  20. [28]

    Buchbinder, M

    N. Buchbinder, M. Feldman, J. Seffi, and R. Schwartz. A tight linear time (1/2)-approximation for unconstrained submodular maximization. SIAM Journal on Computing, 44 0 (5): 0 1384--1402, 2015

    2015

  21. [29]

    Bunton and P

    J. Bunton and P. Tabuada. Give the problem a lift: solving quadratic programs with combinatorial costs. In IEEE Conf.\ on Decision and Control , pages 6941--6946, 2022 a

    2022

  22. [30]

    Bunton and P

    J. Bunton and P. Tabuada. Joint continuous and discrete model selection via submodularity. Journal of Machine Learning Research, 23 0 (329): 0 1--42, 2022 b

    2022

  23. [31]

    A. G. Busetto and J. Lygeros. Experimental design for system identification of boolean control networks in biology. In 53rd IEEE Conference on Decision and Control, pages 5704--5709, 2014

    2014

  24. [32]

    X. Cai, B. Schlotfeldt, K. Khosoussi, N. Atanasov, G. J. Pappas, and J. P. How. Non-monotone energy-aware information gathering for heterogeneous robot teams. In IEEE Int. Conf.\ on Robotics and Automation , pages 8859--8865, 2021

    2021

  25. [33]

    X. Cai, B. Schlotfeldt, K. Khosoussi, N. Atanasov, G. J. Pappas, and J. P. How. Energy-aware, collision-free information gathering for heterogeneous robot teams. IEEE Transactions on Robotics, 39 0 (4): 0 2585--2602, 2023

    2023

  26. [34]

    Calinescu, C

    G. Calinescu, C. Chekuri, M. P \'a l, and J. Vondr \'a k. Maximizing a monotone submodular function subject to a matroid constraint. SIAM Journal on Computing, 40 0 (6): 0 1740--1766, 2011

    2011

  27. [35]

    Castiglia and S

    T. Castiglia and S. Patterson. Distributed submodular maximization with bounded communication cost. In IEEE Conf.\ on Decision and Control , pages 3006--3011, 2019

    2019

  28. [36]

    Chakrabarty, A

    A. Chakrabarty, A. Raghunathan, S. Di Cairano, and C. Danielson. Data-driven estimation of backward reachable and invariant sets for unmodeled systems via active learning. In IEEE Conf.\ on Decision and Control , pages 372--377, 2018

    2018

  29. [37]

    L. F. Chamon, A. Amice, and A. Ribeiro. Matroid-constrained approximately supermodular optimization for near-optimal actuator scheduling. In IEEE Conf.\ on Decision and Control , pages 3391--3398, 2019

    2019

  30. [38]

    L. F. Chamon, G. J. Pappas, and A. Ribeiro. Approximate supermodularity of K alman filter sensor selection. IEEE Transactions on Automatic Control, 66 0 (1): 0 49--63, 2021

    2021

  31. [39]

    L. F. Chamon, A. Amice, and A. Ribeiro. Approximately supermodular scheduling subject to matroid constraints. IEEE Transactions on Automatic Control, 67 0 (3): 0 1384--1396, 2022

    2022

  32. [40]

    P. V. Chanekar and J. Cort \'e s. Encoding impact of network modification on controllability via edge centrality matrix. IEEE Transactions on Control of Network Systems, 9 0 (4): 0 1899--1910, 2022

    1910

  33. [41]

    K. Chen, W. He, Q.-L. Han, M. Xue, and Y. Tang. Leader selection in networks under switching topologies with antagonistic interactions. Automatica, 142: 0 110334, 2022

    2022

  34. [42]

    Y. Chen, L. Zhao, Y. Zhang, S. Huang, and G. Dissanayake. Anchor selection for SLAM based on graph topology and submodular optimization. IEEE Transactions on Robotics, 38 0 (1): 0 329--350, 2021

    2021

  35. [43]

    Cheng, L

    S. Cheng, L. Niu, A. Clark, and R. Poovendran. A submodular energy function approach to controlled islanding with provable stability. In IEEE Conf.\ on Decision and Control , pages 7635--7642, 2023

    2023

  36. [44]

    Choudhury, S

    S. Choudhury, S. Arora, and S. Scherer. The planner ensemble: Motion planning by executing diverse algorithms. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 2389--2395, 2015

    2015

  37. [45]

    Choudhury, A

    S. Choudhury, A. Kapoor, G. Ranade, and D. Dey. Learning to gather information via imitation. In IEEE Int. Conf.\ on Robotics and Automation , pages 908--915, 2017

    2017

  38. [46]

    Chugg and T

    B. Chugg and T. Maehara. Submodular stochastic probing with prices. In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pages 60--66, 2019

    2019

  39. [47]

    A. Clark. A submodular optimization approach to the metric traveling salesman problem with neighborhoods. In IEEE Conf.\ on Decision and Control , pages 3383--3390, 2019

    2019

  40. [48]

    Clark and R

    A. Clark and R. Poovendran. A Submodular Optimization Framework for Leader Selection in Linear Multi-Agent Systems . In IEEE Conf.\ on Decision and Control , pages 3614--3621, 2011

    2011

  41. [49]

    Clark, L

    A. Clark, L. Bushnell, and R. Poovendran. On Leader Selection for Performance and Controllability in Multi-Agent Systems . In IEEE Conf.\ on Decision and Control , pages 86--93, 2012

    2012

  42. [50]

    Clark, B

    A. Clark, B. Alomair, L. Bushnell, and R. Poovendran. Minimizing convergence error in multi-agent systems via leader selection: A supermodular optimization approach. IEEE Transactions on Automatic Control, 59 0 (6): 0 1480--1494, 2014 a

    2014

  43. [51]

    Clark, L

    A. Clark, L. Bushnell, and R. Poovendran. A supermodular optimization framework for leader selection under link noise in linear multi-agent systems. IEEE Transactions on Automatic Control, 59 0 (2): 0 283--296, 2014 b

    2014

  44. [52]

    Clark, B

    A. Clark, B. Alomair, L. Bushnell, and R. Poovendran. Submodularity in dynamics and control of networked systems. Springer, 2016

    2016

  45. [53]

    Clark, B

    A. Clark, B. Alomair, L. Bushnell, and R. Poovendran. Toward synchronization in networks with nonlinear dynamics: A submodular optimization framework. IEEE Transactions on Automatic Control, 62 0 (10): 0 5055--5068, 2017 a

    2017

  46. [54]

    Clark, Q

    A. Clark, Q. Hou, L. Bushnell, and R. Poovendran. A submodular optimization approach to leader-follower consensus in networks with negative edges. In A merican C ontrol C onference , pages 1346--1352, 2017 b

    2017

  47. [55]

    Clark, Q

    A. Clark, Q. Hou, L. Bushnell, and R. Poovendran. Maximizing the smallest eigenvalue of a symmetric matrix: A submodular optimization approach. Automatica, 95: 0 446--454, 2018

    2018

  48. [56]

    Clark, B

    A. Clark, B. Alomair, L. Bushnell, and R. Poovendran. On the structure and computation of random walk times in finite graphs. IEEE Transactions on Automatic Control, 64 0 (11): 0 4470--4483, 2019

    2019

  49. [57]

    G. Como, S. Durand, and F. Fagnani. Optimal targeting in super-modular games. IEEE Transactions on Automatic Control, 67 0 (12): 0 6366--6380, 2022

    2022

  50. [58]

    Conforti and G

    M. Conforti and G. Cornu \'e jols. Submodular set functions, matroids and the greedy algorithm: Tight worst-case bounds and some generalizations of the rado-edmonds theorem. Discrete Applied Mathematics, 7 0 (3): 0 251--274, 1984

    1984

  51. [59]

    Corah and N

    M. Corah and N. Michael. Distributed submodular maximization on partition matroids for planning on large sensor networks. In IEEE Conf.\ on Decision and Control , pages 6792--6799, 2018

    2018

  52. [60]

    Corah and N

    M. Corah and N. Michael. Distributed matroid-constrained submodular maximization for multi-robot exploration: Theory and practice. Autonomous Robots, 43 0 (2): 0 485--501, 2019

    2019

  53. [61]

    Corah and N

    M. Corah and N. Michael. Scalable distributed planning for multi-robot, multi-target tracking. In IEEE/RSJ Int.\ Conf.\ on Intelligent Robots & Systems, pages 437--444, 2021 a

    2021

  54. [62]

    Corah and N

    M. Corah and N. Michael. Volumetric objectives for multi-robot exploration of three-dimensional environments. In IEEE Int. Conf.\ on Robotics and Automation , pages 9043--9050, 2021 b

    2021

  55. [63]

    F. L. Cortesi, T. H. Summers, and J. Lygeros. Submodularity of Energy Related Controllability Metrics . In IEEE Conf.\ on Decision and Control , pages 2883--2888, 2014

    2014

  56. [64]

    Das and D

    A. Das and D. Kempe. Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection. In Proceedings of the 28th International Conference on Machine Learning (ICML), pages 1057--1064, 2011

    2011

  57. [65]

    Das and C

    S. Das and C. Eksin. Approximate submodularity of maximizing anticoordination in network games. In IEEE Conf.\ on Decision and Control , pages 3151--3157, 2022

    2022

  58. [66]

    Das and C

    S. Das and C. Eksin. Average submodularity of maximizing anticoordination in network games. SIAM Journal on Control and Optimization, 62 0 (5): 0 2639--2663, 2024

    2024

  59. [67]

    M. H. de Badyn and M. Mesbahi. Growing controllable networks via whiskering and submodular optimization. In IEEE Conf.\ on Decision and Control , pages 867--872, 2016

    2016

  60. [68]

    A. C. B. De Oliveira, M. Siami, and E. D. Sontag. Bilinear dynamical networks under malicious attack: An efficient edge protection method. In 2021 American Control Conference (ACC), pages 1210--1216, 2021

    2021

  61. [69]

    A. C. B. de Oliveira, M. Siami, and E. D. Sontag. Edge selections in bilinear dynamic networks. IEEE Transactions on Automatic Control, 69 0 (1): 0 331--338, 2024

    2024

  62. [70]

    De Santi, M

    R. De Santi, M. Prajapat, and A. Krause. Global reinforcement learning: Beyond linear and convex rewards via submodular semi-gradient methods. In Proceedings of the 41st International Conference on Machine Learning (ICML), volume 235 of Proceedings of Machine Learning Research...

    2024

  63. [71]

    Dianetti and G

    J. Dianetti and G. Ferrari. Nonzero-sum submodular monotone-follower games: existence and approximation of N ash equilibria. 58 0 (3): 0 1257--1288, 2020

    2020

  64. [72]

    Ding and D

    H. Ding and D. Castan \'o n. Multi-agent discrete search with limited visibility. In IEEE Conf.\ on Decision and Control , pages 108--113, 2017

    2017

  65. [73]

    Ding and D

    H. Ding and D. Casta \ n \'o n. Moving object search with multiple agents. In IEEE Conf.\ on Decision and Control , pages 5746--5752, 2018

    2018

  66. [74]

    Downie, B

    A. Downie, B. Gharesifard, and S. L. Smith. A programming approach for worst-case studies in distributed submodular maximization. In IEEE Conf.\ on Decision and Control , pages 6518--6523, 2022 a

    2022

  67. [75]

    Downie, B

    A. Downie, B. Gharesifard, and S. L. Smith. Submodular maximization with limited function access. IEEE Transactions on Automatic Control, 2022 b

    2022

  68. [76]

    Dressel and M

    L. Dressel and M. J. Kochenderfer. On the optimality of ergodic trajectories for information gathering tasks. In 2018 Annual American Control Conference (ACC), pages 1855--1861, 2018

    2018

  69. [77]

    B. Du, K. Qian, C. Claudel, and D. Sun. Jacobi-style iteration for distributed submodular maximization. IEEE Transactions on Automatic Control, 67 0 (9): 0 4687--4702, 2022

    2022

  70. [78]

    N. Du, Y. Liang, M.-F. Balcan, M. Gomez-Rodriguez, H. Zha, and L. Song. Scalable influence maximization for multiple products in continuous-time diffusion networks. J. Mach. Learn. Res., 18: 0 Paper No. 2, 45, 2017

    2017

  71. [79]

    Dworczak

    P. Dworczak. Mechanism design with aftermarkets: cutoff mechanisms. Econometrica, 88 0 (6): 0 2629--2661, 2020

    2020

  72. [80]

    J. Edmonds. Submodular functions, matroids, and certain polyhedra. In Combinatorial Optimization—Eureka, You Shrink! Papers Dedicated to Jack Edmonds 5th International Workshop Aussois, France, March 5--9, 2001 Revised Papers, pages 11--26. Springer, 2003

    2001

  73. [81]

    Ellis, G

    E. Ellis, G. R. Ghosal, S. J. Russell, A. Dragan, and E. B y k. A generalized acquisition function for preference-based reward learning. In IEEE Int. Conf.\ on Robotics and Automation , pages 2814--2821, 2024

    2024

  74. [82]

    S. M. Elsherif and A. F. Taha. Control node placement and structural controllability of water quality dynamics in drinking networks. Water Resources Research, 61: 0 e2024WR039185, 2025

    2025

  75. [83]

    Feige, V

    U. Feige, V. S. Mirrokni, and J. Vondr \'a k. Maximizing non-monotone submodular functions. SIAM Journal on Computing, 40 0 (4): 0 1133--1153, 2011

    2011

  76. [84]

    Fiscko, S

    C. Fiscko, S. Kar, and B. Sinopoli. Maximizing reachability in factored mdps via near-optimal clustering with applications to control of multi-agent systems. In IEEE Conf.\ on Decision and Control , pages 7970--7975, 2023

    2023

  77. [85]

    Fiscko, S

    C. Fiscko, S. Kar, and B. Sinopoli. Clustered control of transition-independent mdps. IEEE Transactions on Control of Network Systems, 12 0 (3): 0 1881--1893, 2025

    2025

  78. [86]

    M. L. Fisher, G. L. Nemhauser, and L. A. Wolsey. An analysis of approximations for maximizing submodular set functions- II . In Mathematical Programming Study 8, pages 73--87. North-Holland Publishing Company, 1978

    1978

  79. [87]

    Frick, P

    D. Frick, P. G. Sessa, T. A. Wood, and M. Kamgarpour. Exploiting structure of chance constrained programs via submodularity. Automatica, 105: 0 89--95, 2019

    2019

  80. [88]

    Fujishige

    S. Fujishige. Submodular functions and optimization, volume 58. Elsevier, 2005

    2005

  81. [89]

    C. Gao, S. Gu, J. Yu, H. Du, and W. Wu. Adaptive seeding for profit maximization in social networks. Journal of Global Optimization, 82 0 (2): 0 413--432, 2022

    2022

  82. [90]

    Ghaffari Jadidi, J

    M. Ghaffari Jadidi, J. Valls Miro, and G. Dissanayake. Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring. The International Journal of Robotics Research, 38 0 (6): 0 658--685, 2019

    2019

  83. [91]

    Gharesifard and S

    B. Gharesifard and S. L. Smith. Distributed submodular maximization with limited information. IEEE Transactions on Control of Network Systems, 5 0 (4): 0 1635--1645, 2017

    2017

  84. [92]

    Ghasemi and U

    M. Ghasemi and U. Topcu. Online active perception for partially observable markov decision processes with limited budget. In IEEE Conf.\ on Decision and Control , pages 6169--6174, 2019

    2019

  85. [93]

    Ghasemi, A

    M. Ghasemi, A. Hashemi, U. Topcu, and H. Vikalo. On submodularity of quadratic observation selection in constrained networked sensing systems. In A merican C ontrol C onference , pages 4671--4676, 2019

    2019

  86. [94]

    Golovin and A

    D. Golovin and A. Krause. Adaptive submodularity: Theory and applications in active learning and stochastic optimization. Journal of Artificial Intelligence Research, 42: 0 427--486, 2011

    2011

  87. [95]

    Gomes and A

    R. Gomes and A. Krause. Budgeted nonparametric learning from data streams. In ICML, pages 391--398, 2010

    2010

  88. [96]

    Grimsman, J

    D. Grimsman, J. P. Hespanha, and J. R. Marden. Strategic information sharing in greedy submodular maximization. In IEEE Conf.\ on Decision and Control , pages 2722--2727, 2018

    2018

  89. [97]

    Grimsman, M

    D. Grimsman, M. S. Ali, J. P. Hespanha, and J. R. Marden. The impact of information in distributed submodular maximization. IEEE Transactions on Control of Network Systems, 6 0 (4): 0 1334--1343, 2019

    2019

  90. [98]

    Grimsman, M

    D. Grimsman, M. R. Kirchner, J. P. Hespanha, and J. R. Marden. The impact of message passing in agent-based submodular maximization. In IEEE Conf.\ on Decision and Control , pages 530--535, 2020 a

    2020

  91. [99]

    Grimsman, J

    D. Grimsman, J. H. Seaton, J. R. Marden, and P. N. Brown. The cost of denied observation in multiagent submodular optimization. In IEEE Conf.\ on Decision and Control , pages 1666--1671, 2020 b

    2020

  92. [100]

    Grimsman, P

    D. Grimsman, P. N. Brown, and J. R. Marden. Valid utility games with information sharing constraints. In IEEE Conf.\ on Decision and Control , pages 5739--5744, 2022

    2022

  93. [101]

    Grimsman, M

    D. Grimsman, M. R. Kirchner, J. P. Hespanha, and J. R. Marden. The impact of measurement passing in sensor network measurement selection. IEEE Transactions on Control of Network Systems, 10 0 (1): 0 112--123, 2023

    2023

  94. [102]

    B. Guo, O. Karaca, T. Summers, and M. Kamgarpour. Actuator placement for optimizing network performance under controllability constraints. In IEEE Conf.\ on Decision and Control , pages 7140--7147, 2019

    2019

  95. [103]

    B. Guo, O. Karaca, S. Azhdari, M. Kamgarpour, and G. Ferrari-Trecate. Actuator placement for structural controllability beyond strong connectivity and towards robustness. In IEEE Conf.\ on Decision and Control , pages 5294--5299, 2021 a

    2021

  96. [104]

    B. Guo, O. Karaca, T. Summers, and M. Kamgarpour. Actuator placement under structural controllability using forward and reverse greedy algorithms. IEEE Transactions on Automatic Control, 66 0 (12): 0 5845--5860, 2021 b

    2021

  97. [105]

    J. Guo, T. Chen, and W. Wu. Continuous activity maximization in online social networks. IEEE Transactions on Network Science and Engineering, 7 0 (4): 0 2775--2786, 2020 a

    2020

  98. [106]

    J. Guo, Y. Li, and W. Wu. Targeted protection maximization in social networks. IEEE Transactions on Network Science and Engineering, 7 0 (3): 0 1645--1655, 2020 b

    2020

  99. [107]

    Harshaw, E

    C. Harshaw, E. Kazemi, M. Feldman, and A. Karbasi. The power of subsampling in submodular maximization. Mathematics of Operations Research, 47 0 (2): 0 1365--1393, 2022

    2022

  100. [108]

    Hartman and J

    D. Hartman and J. S. Baras. Near-optimal solution to the non-uniform sampling problem in K alman filtering. In IEEE Conf.\ on Decision and Control , pages 6404--6411, 2019

    2019

  101. [109]

    Hashemi, O

    A. Hashemi, O. F. Kilic, and H. Vikalo. Near-optimal distributed estimation for a network of sensing units operating under communication constraints. In IEEE Conf.\ on Decision and Control , pages 2890--2895, 2018

    2018

  102. [110]

    Hashemi, M

    A. Hashemi, M. Ghasemi, H. Vikalo, and U. Topcu. Randomized greedy sensor selection: Leveraging weak submodularity. IEEE Transactions on Automatic Control, 66 0 (1): 0 199--212, 2020

    2020

  103. [111]

    Hassani, M

    H. Hassani, M. Soltanolkotabi, and A. Karbasi. Gradient methods for submodular maximization. Advances in Neural Information Processing Systems, 30, 2017

    2017

  104. [112]

    Hazan, S

    E. Hazan, S. Safra, and O. Schwartz. On the complexity of approximating k-set packing. computational complexity, 15 0 (1): 0 20--39, 2006

    2006

  105. [113]

    He and D

    X. He and D. Kempe. Price of anarchy for the n -player competitive cascade game with submodular activation functions. In International Conference on Web and Internet Economics, pages 232--248. Springer, 2013

    2013

  106. [114]

    J. P. Hespanha and D. Garagi \'c . Optimal sensor selection for binary detection based on stochastic submodular optimization. In IEEE Conf.\ on Decision and Control , pages 3464--3470, 2020

    2020

  107. [115]

    Hibbard, A

    M. Hibbard, A. Hashemi, T. Tanaka, and U. Topcu. Randomized greedy algorithms for sensor selection in large-scale satellite constellations. In 2023 American Control Conference (ACC), pages 4276--4283, 2023

    2023

  108. [116]

    Hollinger, S

    G. Hollinger, S. Singh, J. Djugash, and A. Kehagias. Efficient multi-robot search for a moving target. The International Journal of Robotics Research, 28 0 (2): 0 201--219, 2009

    2009

  109. [117]

    G. A. Hollinger and G. S. Sukhatme. Sampling-based robotic information gathering algorithms. The International Journal of Robotics Research, 33 0 (9): 0 1271--1287, 2014

    2014

  110. [118]

    I.-H. Hou, A. Truong, S. Chakraborty, and P. Kumar. Optimality of Periodwise Static Priority Policies in Real-Time Communications . In IEEE Conf.\ on Decision and Control , pages 5047--5051, 2011

    2011

  111. [119]

    Hou and A

    Q. Hou and A. Clark. Robust maximization of correlated submodular functions. In IEEE Conf.\ on Decision and Control , pages 7177--7183, 2019

    2019

  112. [120]

    Hou and A

    Q. Hou and A. Clark. Robust maximization of correlated submodular functions under cardinality and matroid constraints. IEEE Transactions on Automatic Control, 66 0 (12): 0 6148--6155, 2021

    2021

  113. [121]

    D. S. Hunter and T. Zaman. Optimizing opinions with stubborn agents. Operations Research, 70 0 (4): 0 2119--2137, 2022

    2022

  114. [122]

    Jaleel and J

    H. Jaleel and J. S. Shamma. Distributed submodular minimization and motion planning over discrete state space. IEEE Transactions on Control of Network Systems, 7 0 (2): 0 932--943, 2020

    2020

  115. [123]

    Javdani, M

    S. Javdani, M. Klingensmith, J. A. Bagnell, N. S. Pollard, and S. S. Srinivasa. Efficient touch based localization through submodularity. In IEEE Int. Conf.\ on Robotics and Automation , pages 1828--1835, 2013

    2013

  116. [124]

    S. T. Jawaid and S. L. Smith. The maximum traveling salesman problem with submodular rewards. In A merican C ontrol C onference , pages 3997--4002, 2013

    2013

  117. [125]

    S. T. Jawaid and S. L. Smith. Informative path planning as a maximum traveling salesman problem with submodular rewards. Discrete Applied Mathematics, 186: 0 112--127, 2015 a

    2015

  118. [126]

    S. T. Jawaid and S. L. Smith. Submodularity and greedy algorithms in sensor scheduling for linear dynamical systems. Automatica, 61: 0 282--288, 2015 b

    2015

  119. [127]

    J. Jiao, Y. Zhu, H. Ye, H. Huang, P. Yun, L. Jiang, L. Wang, and M. Liu. Greedy-based feature selection for efficient L i DAR SLAM . In IEEE Int. Conf.\ on Robotics and Automation , pages 5222--5228, 2021

    2021

  120. [128]

    Johnson, H.-L

    L. Johnson, H.-L. Choi, S. Ponda, and J. P. How. Allowing non-submodular score functions in distributed task allocation . In IEEE Conf.\ on Decision and Control , pages 4702--4708, 2012

    2012

  121. [129]

    Jorgensen and M

    S. Jorgensen and M. Pavone. The matroid team surviving orienteers problem and its variants: Constrained routing of heterogeneous teams with risky traversal. The International Journal of Robotics Research, 43 0 (1): 0 34--52, 2024

    2024

  122. [130]

    Jorgensen, R

    S. Jorgensen, R. H. Chen, M. B. Milam, and M. Pavone. The risk-sensitive coverage problem: Multi-robot routing under uncertainty with service level and survival constraints. In IEEE Conf.\ on Decision and Control , pages 925--932, 2017

    2017

  123. [131]

    Joshi and S

    S. Joshi and S. Boyd. Sensor selection via convex optimization. IEEE Transactions on Signal Processing, 57 0 (2): 0 451--462, 2009

    2009

  124. [132]

    Karaca and M

    O. Karaca and M. Kamgarpour. Game theoretic analysis of electricity market auction mechanisms. In IEEE Conf.\ on Decision and Control , pages 6211--6216, 2017

    2017

  125. [133]

    Karaca and M

    O. Karaca and M. Kamgarpour. Exploiting weak supermodularity for coalition-proof mechanisms. In IEEE Conf.\ on Decision and Control , pages 1118--1123, 2018

    2018

  126. [134]

    R. M. Karp. On the computational complexity of combinatorial problems. Networks, 5 0 (1): 0 45--68, 1975

    1975

  127. [135]

    Kaveti, M

    P. Kaveti, M. Giamou, H. Singh, and D. M. Rosen. Oasis: Optimal arrangements for sensing in SLAM . In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 13818--13824, 2024

    2024

  128. [136]

    Kazemi, S

    E. Kazemi, S. Minaee, M. Feldman, and A. Karbasi. Regularized submodular maximization at scale. In International Conference on Machine Learning, pages 5356--5366, 2021

    2021

  129. [137]

    M. H. Kazma and A. F. Taha. Observability for nonlinear systems: Connecting variational dynamics, L yapunov exponents, and empirical gramians. arXiv preprint arXiv:2402.14711, 2024

    2024

  130. [138]

    M. H. Kazma, S. A. Nugroho, A. Haber, and A. F. Taha. State-robust observability measures for sensor selection in nonlinear dynamic systems. In IEEE Conf.\ on Decision and Control , pages 8418--8426, 2023

    2023

  131. [139]

    M. H. Kazma, S. M. Elsherif, and A. F. Taha. Observability and generalized sensor placement for nonlinear quality models in drinking water networks. arXiv preprint arXiv:2411.04202, 2024

    2024

  132. [140]

    Kempe, J

    D. Kempe, J. Kleinberg, and \'E . Tardos. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137--146, 2003

    2003

  133. [141]

    Khuller, A

    S. Khuller, A. Moss, and J. S. Naor. The budgeted maximum coverage problem. Information Processing Letters, 70 0 (1): 0 39--45, 1999

    1999

  134. [142]

    S. S. Kia. Submodular maximization subject to uniform and partition matroids: From theory to practical applications and distributed solutions. arXiv preprint arXiv:2501.01071, 2025

    2025

  135. [143]

    Kim and M

    S.-K. Kim and M. Likhachev. Planning for grasp selection of partially occluded objects. In IEEE Int. Conf.\ on Robotics and Automation , pages 3971--3978, 2016

    2016

  136. [144]

    Konda, D

    R. Konda, D. Grimsman, and J. R. Marden. Execution order matters in greedy algorithms with limited information. In A merican C ontrol C onference , pages 1305--1310, 2022

    2022

  137. [145]

    Konda, R

    R. Konda, R. Chandan, D. Grimsman, and J. R. Marden. Optimal utility design of greedy algorithms in resource allocation games. IEEE Transactions on Automatic Control, 2024

    2024

  138. [146]

    Korula, V

    N. Korula, V. Mirrokni, and M. Zadimoghaddam. Online submodular welfare maximization: greedy beats 1/2 in random order. SIAM Journal on Computing, 47 0 (3): 0 1056--1086, 2018

    2018

  139. [147]

    Krause and D

    A. Krause and D. Golovin. Submodular function maximization. Tractability: Practical Approaches to Hard Problems, pages 71--104, 2014

    2014

  140. [148]

    Krause, H

    A. Krause, H. B. McMahan, C. Guestrin, and A. Gupta. Robust submodular observation selection. Journal of Machine Learning Research, 9 0 (93): 0 2761--2801, 2008 a

    2008

  141. [149]

    Krause, A

    A. Krause, A. Singh, and C. Guestrin. Near-optimal sensor placements in G aussian processes: Theory, efficient algorithms and empirical studies. Journal of Machine Learning Research, 9 0 (8): 0 235--284, 2008 b

    2008

  142. [150]

    Krause, R

    A. Krause, R. Rajagopal, A. Gupta, and C. Guestrin. Simultaneous optimization of sensor placements and balanced schedules. IEEE Transactions on Automatic Control, 56 0 (10): 0 2390--2405, 2011

    2011

  143. [151]

    Krishnamurthy

    V. Krishnamurthy. Interval dominance based structural results for M arkov decision process. Automatica, 153: 0 Paper No. 111024, 8, 2023

    2023

  144. [152]

    Krishnamurthy, A

    V. Krishnamurthy, A. Aprem, and S. Bhatt. Multiple stopping time pomdps: Structural results & application in interactive advertising on social media. Automatica, 95: 0 385--398, 2018

    2018

  145. [153]

    Kyriakis, S

    P. Kyriakis, S. Pequito, and P. Bogdan. Actuator placement for heterogeneous complex dynamical networks with long-term memory. In 2020 American Control Conference (ACC), pages 4671--4676, 2020 a

    2020

  146. [154]

    Kyriakis, S

    P. Kyriakis, S. Pequito, and P. Bogdan. On the stability and fairness of submodular allocations. In IEEE Conf.\ on Decision and Control , pages 2648--2653, 2020 b

    2020

  147. [155]

    Laszka, Y

    A. Laszka, Y. Vorobeychik, and X. Koutsoukos. Resilient observation selection in adversarial settings. In IEEE Conf.\ on Decision and Control , pages 7416--7421, 2015

    2015

  148. [156]

    C. S. Laurent and R. V. Cowlagi. Near-optimal task-driven sensor network configuration. Automatica, 152: 0 110966, 2023

    2023

  149. [157]

    Lebedev, P

    D. Lebedev, P. Goulart, and K. Margellos. Gradient-bounded dynamic programming for submodular and concave extensible value functions with probabilistic performance guarantees. Automatica, 135: 0 109897, 2022

    2022

  150. [158]

    J. Lee, M. Sviridenko, and J. Vondr \'a k. Submodular maximization over multiple matroids via generalized exchange properties. Mathematics of Operations Research, 35 0 (4): 0 795--806, 2010

    2010

  151. [159]

    Lesage-Landry and J

    A. Lesage-Landry and J. Pallage. Online dynamic submodular optimization. Automatica, 167: 0 111758, 2024

    2024

  152. [160]

    Leskovec, A

    J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 420--429, 2007

    2007

  153. [161]

    M. Li, A. Richards, and M. Sooriyabandara. Asynchronous reliability-aware multi-uav coverage path planning. In IEEE Int. Conf.\ on Robotics and Automation , pages 10023--10029, 2021

    2021

  154. [162]

    Y. Li, A. S. Mehr, and T. Chen. Multi-sensor transmission power control for remote estimation through a SINR -based communication channel. Automatica, 101: 0 78--86, 2019

    2019

  155. [163]

    Li and K.-S

    Y.-S. Li and K.-S. Tseng. Computation-aware multi-object search in 3d space using submodular tree. In IEEE Int. Conf.\ on Robotics and Automation , pages 5956--5962, 2024 a

    2024

  156. [164]

    Li and K.-S

    Y.-S. Li and K.-S. Tseng. Multi-robot search in a 3d environment with intersection system constraints. In IEEE Int. Conf.\ on Robotics and Automation , pages 5963--5969, 2024 b

    2024

  157. [165]

    Z. W. Lim, D. Hsu, and W. S. Lee. Adaptive informative path planning in metric spaces. The International Journal of Robotics Research, 35 0 (5): 0 585--598, 2016

    2016

  158. [166]

    Liu and R

    J. Liu and R. K. Williams. Optimal intermittent deployment and sensor selection for environmental sensing with multi-robot teams. In IEEE Int. Conf.\ on Robotics and Automation , pages 1078--1083, 2018

    2018

  159. [167]

    Liu and R

    J. Liu and R. K. Williams. Monitoring over the long term: Intermittent deployment and sensing strategies for multi-robot teams. In IEEE Int. Conf.\ on Robotics and Automation , pages 7733--7739, 2020

    2020

  160. [168]

    S. Liu, Y. Zhao, and Q. Zhu. Understanding the interplay between herd behaviors and epidemic spreading using federated evolutionary games. In A merican C ontrol C onference , pages 593--598, 2022

    2022

  161. [169]

    Liu and B

    X. Liu and B. Sinopoli. On partial observability of large scale linear systems: A structured systems approach. In IEEE Conf.\ on Decision and Control , pages 4655--4661, 2018

    2018

  162. [170]

    Y. Liu, E. K. Chong, and A. Pezeshki. Bounding the greedy strategy in finite-horizon string optimization. In IEEE Conf.\ on Decision and Control , pages 3900--3905, 2015

    2015

  163. [171]

    Liu, J.-J

    Y.-Y. Liu, J.-J. Slotine, and A.-L. Barab \'a si. Controllability of complex networks. nature, 473 0 (7346): 0 167--173, 2011

    2011

  164. [172]

    Z. Liu, A. Clark, P. Lee, L. Bushnell, D. Kirschen, and R. Poovendran. Mingen: Minimal generator set selection for small signal stability in power systems: A submodular framework. In IEEE Conf.\ on Decision and Control , pages 4122--4129, 2016

    2016

  165. [173]

    Lov \'a sz

    L. Lov \'a sz. Submodular functions and convexity. In Mathematical Programming The State of the Art, pages 235--257. Springer, 1983

    1983

  166. [174]

    W. Luan, Y. Yang, C. Ferm \"u ller, and J. S. Baras. Fast task-specific target detection via graph based constraints representation and checking. In IEEE Int. Conf.\ on Robotics and Automation , pages 3984--3991, 2017

    2017

  167. [175]

    R. Luo, B. Nettasinghe, and V. Krishnamurthy. Controlling segregation in social network dynamics as an edge formation game. IEEE Transactions on Network Science and Engineering, 9 0 (4): 0 2317--2329, 2022

    2022

  168. [176]

    W. Luo, S. S. Khatib, S. Nagavalli, N. Chakraborty, and K. Sycara. Distributed knowledge leader selection for multi-robot environmental sampling under bandwidth constraints. In IEEE/RSJ Int.\ Conf.\ on Intelligent Robots & Systems, pages 5751--5757, 2016

    2016

  169. [177]

    Mackin and S

    E. Mackin and S. Patterson. Optimizing the coherence of composite networks. In 2017 American Control Conference (ACC), pages 4334--4340, 2017

    2017

  170. [178]

    Mackin and S

    E. Mackin and S. Patterson. Second order consensus with absolute information. In IEEE Conf.\ on Decision and Control , pages 4523--4528, 2018

    2018

  171. [179]

    Mackin and S

    E. Mackin and S. Patterson. Submodular optimization for consensus networks with noise-corrupted leaders. IEEE Transactions on Automatic Control, 64 0 (7): 0 3054--3059, 2019

    2019

  172. [180]

    V. S. Mai and E. H. Abed. Optimizing leader influence in networks through selection of direct followers. IEEE Transactions on Automatic Control, 64 0 (3): 0 1280--1287, 2019

    2019

  173. [181]

    Maillet, H

    Q. Maillet, H. Xu, N. Ozay, and R. M. Murray. Dynamic State Estimation in Distributed Aircraft Electric Control Systems via Adaptive Submodularity . In IEEE Conf.\ on Decision and Control , pages 5497--5503, 2013

    2013

  174. [182]

    J. R. Marden. State based potential games. Automatica, 48 0 (12): 0 3075--3088, 2012

    2012

  175. [183]

    J. R. Marden. The Role of Information in Multiagent Coordination . In IEEE Conf.\ on Decision and Control , pages 445--450, 2014

    2014

  176. [184]

    J. R. Marden. The role of information in distributed resource allocation. IEEE Transactions on Control of Network Systems, 4 0 (3): 0 654--664, 2017

    2017

  177. [185]

    J. R. Marden and T. Roughgarden. Generalized efficiency bounds in distributed resource allocation. IEEE Transactions on Automatic Control, 59 0 (3): 0 571--584, 2014

    2014

  178. [186]

    J. R. Marden and A. Wierman. Distributed welfare games. Operations Research, 61 0 (1): 0 155--168, 2013

    2013

  179. [187]

    Mehr and R

    N. Mehr and R. Horowitz. A submodular approach for optimal sensor placement in traffic networks. In A merican C ontrol C onference , pages 6353--6358, 2018

    2018

  180. [188]

    T. Miki, M. Popovi \'c , A. Gawel, G. Hitz, and R. Siegwart. Multi-agent time-based decision-making for the search and action problem. In IEEE Int. Conf.\ on Robotics and Automation , pages 2365--2372, 2018

    2018

  181. [189]

    Milo s evi \'c , A

    J. Milo s evi \'c , A. Teixeira, K. H. Johansson, and H. Sandberg. Actuator security indices based on perfect undetectability: Computation, robustness, and sensor placement. IEEE Transactions on Automatic Control, 65 0 (9): 0 3816--3831, 2020

    2020

  182. [190]

    M. Minoux. Accelerated greedy algorithms for maximizing submodular set functions. Optimization Techniques, pages 234--243, 1978

    1978

  183. [191]

    Mirzasoleiman, A

    B. Mirzasoleiman, A. Badanidiyuru, A. Karbasi, J. Vondr \'a k, and A. Krause. Lazier than lazy greedy. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 29, pages 1812--1818, 2015

    2015

  184. [192]

    Mirzasoleiman, A

    B. Mirzasoleiman, A. Karbasi, R. Sarkar, and A. Krause. Distributed submodular maximization. The Journal of Machine Learning Research, 17 0 (1): 0 8330--8373, 2016

    2016

  185. [193]

    Mossel and S

    E. Mossel and S. Roch. Submodularity of influence in social networks: From local to global. SIAM Journal on Computing, 39 0 (6): 0 2176--2188, 2010

    2010

  186. [194]

    Mualem, M

    L. Mualem, M. Tukan, and M. Feldman. Bridging the gap between general and down-closed convex sets in submodular maximization. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI), pages 1926--1934, 2024

    1926

  187. [195]

    G. L. Nemhauser and L. A. Wolsey. Best algorithms for approximating the maximum of a submodular set function. Mathematics of Operations Research, 3 0 (3): 0 177--188, 1978

    1978

  188. [196]

    G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. An analysis of approximations for maximizing submodular set functions- I . Mathematical Programming, 14 0 (1): 0 265--294, 1978

    1978

  189. [197]

    Q. Ni, J. Guo, W. Wu, H. Wang, and J. Wu. Continuous influence-based community partition for social networks. IEEE Transactions on Network Science and Engineering, 9 0 (3): 0 1187--1197, 2022

    2022

  190. [198]

    Nishida and K

    S. Nishida and K. Okano. Sparsity-constrained linear quadratic regulation problem: Greedy approach with performance guarantee. In E uropean C ontrol C onference , pages 3612--3617, 2024

    2024

  191. [199]

    Olshevsky

    A. Olshevsky. Minimal controllability problems. IEEE Transactions on Control of Network Systems, 1 0 (3): 0 249--258, 2014

    2014

  192. [200]

    Olshevsky

    A. Olshevsky. On (non) supermodularity of average control energy. IEEE Transactions on Control of Network Systems, 5 0 (3): 0 1177--1181, 2018

    2018

  193. [201]

    J. B. Orlin, A. S. Schulz, and R. Udwani. Robust monotone submodular function maximization. In International Conference on Integer Programming and Combinatorial Optimization, pages 312--324. Springer, 2016

    2016

  194. [202]

    J. G. Oxley. Matroid theory, volume 3. Oxford University Press, USA, 2006

    2006

  195. [203]

    Paccagnan and J

    D. Paccagnan and J. R. Marden. The importance of system-level information in multiagent systems design: Cardinality and covering problems. IEEE Transactions on Automatic Control, 64 0 (8): 0 3253--3267, 2019

    2019

  196. [204]

    Paccagnan and J

    D. Paccagnan and J. R. Marden. Utility design for distributed resource allocation—part II : Applications to submodular, covering, and supermodular problems. IEEE Transactions on Automatic Control, 67 0 (2): 0 618--632, 2022

    2022

  197. [205]

    Paccagnan, R

    D. Paccagnan, R. Chandan, and J. R. Marden. Utility design for distributed resource allocation—part I : Characterizing and optimizing the exact price of anarchy. IEEE Transactions on Automatic Control, 65 0 (11): 0 4616--4631, 2020

    2020

  198. [206]

    S. Park, C. Ratti, and D. Rus. Adaptive sensor selection for monitoring stochastic processes. In IEEE Conf.\ on Decision and Control , pages 6766--6772, 2018

    2018

  199. [207]

    Pasqualetti, S

    F. Pasqualetti, S. Zampieri, and F. Bullo. Controllability metrics, limitations and algorithms for complex networks. IEEE Transactions on Control of Network Systems, 1 0 (1): 0 40--52, 2014

    2014

  200. [208]

    Pequito, G

    S. Pequito, G. Ramos, S. Kar, A. P. Aguiar, and J. Ramos. The robust minimal controllability problem. Automatica, 82: 0 261--268, 2017

    2017

  201. [209]

    L. S. Perelman, W. Abbas, X. Koutsoukos, and S. Amin. Sensor placement for fault location identification in water networks: A minimum test cover approach. Automatica, 72: 0 166--176, 2016

    2016

  202. [210]

    C. V. Pham, D. V. Pham, B. Q. Bui, and A. V. Nguyen. Minimum budget for misinformation detection in online social networks with provable guarantees. Optim. Lett., 16 0 (2): 0 515--544, 2022

    2022

  203. [211]

    Prajapat, M

    M. Prajapat, M. Mutn \`y , M. N. Zeilinger, and A. Krause. Submodular reinforcement learning. In International Conference on Learning Representations (ICLR), pages 15190--15212, 2024

    2024

  204. [212]

    Prasad, J

    A. Prasad, J. Hudack, S. Mou, and S. Sundaram. Policies for risk-aware sensor data collection by mobile agents. Automatica, 142: 0 110391, 2022

    2022

  205. [213]

    B. Qi. On maximizing sums of non-monotone submodular and linear functions. Algorithmica, 86 0 (4): 0 1080--1134, 2024

    2024

  206. [214]

    J. Qin, J. Mether, J.-Y. Joo, R. Rajagopal, K. Poolla, and P. Varaiya. Automatic power exchange for distributed energy resource networks: Flexibility scheduling and pricing. In IEEE Conf.\ on Decision and Control , pages 1572--1579, 2018

    2018

  207. [215]

    J. Qin, I. Yang, and R. Rajagopal. Submodularity of storage placement optimization in power networks. IEEE Transactions on Automatic Control, 64 0 (8): 0 3268--3283, 2019

    2019

  208. [216]

    G. Qu, D. Brown, and N. Li. Distributed greedy algorithm for multi-agent task assignment problem with submodular utility functions. Automatica, 105: 0 206--215, 2019

    2019

  209. [217]

    M. A. Rahimian and V. M. Preciado. Detection and isolation of failures in directed networks of lti systems. IEEE Transactions on Control of Network Systems, 2 0 (2): 0 183--192, 2015

    2015

  210. [218]

    Rezazadeh and S

    N. Rezazadeh and S. S. Kia. Multi-agent maximization of a monotone submodular function via maximum consensus. In IEEE Conf.\ on Decision and Control , pages 1238--1243, 2021 a

    2021

  211. [219]

    Rezazadeh and S

    N. Rezazadeh and S. S. Kia. A sub-modular receding horizon solution for mobile multi-agent persistent monitoring. Automatica, 127: 0 109460, 2021 b

    2021

  212. [220]

    Rezazadeh and S

    N. Rezazadeh and S. S. Kia. Distributed submodular maximization: trading performance for privacy. In IEEE Conf.\ on Decision and Control , pages 5953--5958, 2022

    2022

  213. [221]

    Rezazadeh and S

    N. Rezazadeh and S. S. Kia. Distributed strategy selection: A submodular set function maximization approach. Automatica, 153: 0 111000, 2023

    2023

  214. [222]

    Robey, A

    A. Robey, A. Adibi, B. Schlotfeldt, H. Hassani, and G. J. Pappas. Optimal algorithms for submodular maximization with distributed constraints. In Learning for Dynamics and Control, pages 150--162, 2021

    2021

  215. [223]

    J. B. Rosen. Existence and uniqueness of equilibrium points for concave n-person games. Econometrica: Journal of the Econometric Society, pages 520--534, 1965

    1965

  216. [224]

    Sahabandu, S

    D. Sahabandu, S. Moothedath, J. Allen, A. Clark, L. Bushnell, W. Lee, and R. Poovendran. Dynamic information flow tracking games for simultaneous detection of multiple attackers. In IEEE Conf.\ on Decision and Control , pages 567--574, 2019

    2019

  217. [225]

    Sahabandu, A

    D. Sahabandu, A. Clark, L. Bushnell, and R. Poovendran. Submodular input selection for synchronization in kuramoto networks. In IEEE Conf.\ on Decision and Control , pages 5840--5847, 2020

    2020

  218. [226]

    Sahabandu, L

    D. Sahabandu, L. Niu, A. Clark, and R. Poovendran. Scalable planning in multi-agent mdps. In IEEE Conf.\ on Decision and Control , pages 5932--5939, 2021

    2021

  219. [227]

    Schlotfeldt, V

    B. Schlotfeldt, V. Tzoumas, and G. J. Pappas. Resilient active information acquisition with teams of robots. IEEE Transactions on Robotics, 38 0 (1): 0 244--261, 2022

    2022

  220. [228]

    Schoof, A

    E. Schoof, A. Chapman, and M. Mesbahi. Efficient leader selection for translation and scale of a bearing-compass formation. In IEEE Int. Conf.\ on Robotics and Automation , pages 1816--1821, 2015

    2015

  221. [229]

    Schrijver

    A. Schrijver. A combinatorial algorithm minimizing submodular functions in strongly polynomial time. Journal of Combinatorial Theory, Series B, 80 0 (2): 0 346--355, 2000

    2000

  222. [230]

    Segui-Gasco, H.-S

    P. Segui-Gasco, H.-S. Shin, A. Tsourdos, and V. Segui. Decentralised submodular multi-robot task allocation. In IEEE/RSJ Int.\ Conf.\ on Intelligent Robots & Systems, pages 2829--2834, 2015

    2015

  223. [231]

    Shamaiah, S

    M. Shamaiah, S. Banerjee, and H. Vikalo. Greedy Sensor Selection: Leveraging Submodularity . In IEEE Conf.\ on Decision and Control , pages 2572--2577, 2010

    2010

  224. [232]

    Shames and T

    I. Shames and T. H. Summers. Rigid network design via submodular set function optimization. IEEE Transactions on Network Science and Engineering, 2 0 (3): 0 84--96, 2015

    2015

  225. [233]

    rigid network design via submodular set function optimization

    I. Shames and T. H. Summers. Corrections to “rigid network design via submodular set function optimization”. IEEE Transactions on Network Science and Engineering, 7 0 (3): 0 2114--2114, 2020

    2020

  226. [234]

    Shi and G

    G. Shi and G. S. Sukhatme. Inverse submodular maximization with application to human-in-the-loop multi-robot multi-objective coverage control. In IEEE/RSJ Int.\ Conf.\ on Intelligent Robots & Systems, pages 8921--8928, 2024

    2024

  227. [235]

    Shi and P

    G. Shi and P. Tokekar. Decision-oriented learning with differentiable submodular maximization for vehicle routing problem. In IEEE/RSJ Int.\ Conf.\ on Intelligent Robots & Systems, pages 11135--11140, 2023

    2023

  228. [236]

    G. Shi, I. E. Rabban, L. Zhou, and P. Tokekar. Communication-aware multi-robot coordination with submodular maximization. In IEEE Int. Conf.\ on Robotics and Automation , pages 8955--8961, 2021

    2021

  229. [237]

    G. Shi, L. Zhou, and P. Tokekar. Robust multiple-path orienteering problem: Securing against adversarial attacks. IEEE Transactions on Robotics, 39 0 (3): 0 2060--2077, 2023

  230. [238]

    Siami, A

    M. Siami, A. Olshevsky, and A. Jadbabaie. Deterministic and randomized actuator scheduling with guaranteed performance bounds. IEEE Transactions on Automatic Control, 66 0 (4): 0 1686--1701, 2021

    2021

  231. [239]

    V. L. Silva, L. F. Chamon, and A. Ribeiro. Model predictive selection: A receding horizon scheme for actuator selection. In 2019 American Control Conference (ACC), pages 347--353, 2019

    2019

  232. [240]

    Singh, S

    P. Singh, S. Z. Yong, and E. Frazzoli. Supermodular batch state estimation in optimal sensor scheduling. IEEE Control Syst. Lett., 1 0 (2): 0 292--297, 2017

    2017

  233. [241]

    M. V. Srighakollapu, R. Kalaimani, and R. Pasumarthy. Optimizing average controllability of networked systems. In IEEE Conf.\ on Decision and Control , pages 2066--2071, 2019

  234. [242]

    M. V. Srighakollapu, R. K. Kalaimani, and R. Pasumarthy. Optimizing network topology for average controllability. Systems Control Lett., 158: 0 Paper No. 105061, 10, 2021

    2021

  235. [243]

    M. V. Srighakollapu, R. K. Kalaimani, and R. Pasumarthy. Optimizing driver nodes for structural controllability of temporal networks. IEEE Transactions on Control of Network Systems, 9 0 (1): 0 380--389, 2022

    2022

  236. [244]

    Streeter and D

    M. Streeter and D. Golovin. An online algorithm for maximizing submodular functions. Advances in Neural Information Processing Systems, 21, 2008

    2008

  237. [245]

    J. Suh, K. Cho, and S. Oh. Efficient graph-based informative path planning using cross entropy. In IEEE Conf.\ on Decision and Control , pages 5894--5899, 2016

    2016

  238. [246]

    T. Summers. Actuator placement in networks using optimal control performance metrics. In IEEE Conf.\ on Decision and Control , pages 2703--2708, 2016

    2016

  239. [247]

    Summers and M

    T. Summers and M. Kamgarpour. Performance guarantees for greedy maximization of non-submodular controllability metrics. In E uropean C ontrol C onference , pages 2796--2801, 2019

    2019

  240. [248]

    T. H. Summers, F. L. Cortesi, and J. Lygeros. On submodularity and controllability in complex dynamical networks. IEEE Transactions on Control of Network Systems, 3 0 (1): 0 91--101, 2016

    2016

  241. [249]

    C. Sun, S. Welikala, and C. G. Cassandras. Optimal composition of heterogeneous multi-agent teams for coverage problems with performance bound guarantees. Automatica, 117: 0 108961, 2020 a

    2020

  242. [250]

    H. Sun, D. Grimsman, and J. R. Marden. Distributed submodular maximization with parallel execution. In A merican C ontrol C onference , pages 1477--1482, 2020 b

    2020

  243. [251]

    X. Sun, C. G. Cassandras, and X. Meng. Exploiting submodularity to quantify near-optimality in multi-agent coverage problems. Automatica, 100: 0 349--359, 2019

    2019

  244. [252]

    Suresh, A

    K. Suresh, A. Rauniyar, M. Corah, and S. Scherer. Greedy perspectives: Multi-drone view planning for collaborative perception in cluttered environments. In IEEE/RSJ Int.\ Conf.\ on Intelligent Robots & Systems, pages 10990--10997, 2024

    2024

  245. [253]

    Sviridenko

    M. Sviridenko. A note on maximizing a submodular set function subject to a knapsack constraint. Operations Research Letters, 32 0 (1): 0 41--43, 2004

    2004

  246. [254]

    Sviridenko, J

    M. Sviridenko, J. Vondr \'a k, and J. Ward. Optimal approximation for submodular and supermodular optimization with bounded curvature. Mathematics of Operations Research, 42 0 (4): 0 1197--1218, 2017

    2017

  247. [255]

    S. Tan, J. L\"u, and Z. Lin. Emerging behavioral consensus of evolutionary dynamics on complex networks. SIAM Journal on Control and Optimization, 54 0 (6): 0 3258--3272, 2016

    2016

  248. [256]

    Testa, I

    A. Testa, I. Notarnicola, and G. Notarstefano. Distributed submodular minimization over networks: a greedy column generation approach. In IEEE Conf.\ on Decision and Control , pages 4945--4950, 2018

    2018

  249. [257]

    Thorne, N

    D. Thorne, N. Chan, Y. Ma, C. S. Robison, P. R. Osteen, and B. T. Lopez. Submodular optimization for keyframe selection & usage in slam. In IEEE International Conference on Robotics and Automation (ICRA), pages 5033--5039, 2025

    2025

  250. [258]

    Tihanyi, Y

    D. Tihanyi, Y. Lu, O. Karaca, and M. Kamgarpour. Multi-robot task allocation for safe planning against stochastic hazard dynamics. In E uropean C ontrol C onference , pages 1--6, 2023

    2023

  251. [259]

    Tohidi, R

    E. Tohidi, R. Amiri, M. Coutino, D. Gesbert, G. Leus, and A. Karbasi. Submodularity in action: From machine learning to signal processing applications. IEEE Signal Processing Magazine, 37 0 (5): 0 120--133, 2020

    2020

  252. [260]

    D. M. Topkis. Equilibrium points in nonzero-sum n-person submodular games. SIAM Journal on Control and Optimization, 17 0 (6): 0 773--787, 1979

    1979

  253. [261]

    Tzoumas, A

    V. Tzoumas, A. Jadbabaie, and G. J. Pappas. Near-optimal sensor scheduling for batch state estimation: Complexity, algorithms, and limits. In IEEE Conf.\ on Decision and Control , pages 2695--2702, 2016 a

    2016

  254. [262]

    Tzoumas, A

    V. Tzoumas, A. Jadbabaie, and G. J. Pappas. Sensor placement for optimal K alman filtering: Fundamental limits, submodularity, and algorithms. In 2016 American control conference (ACC), pages 191--196, 2016 b

    2016

  255. [263]

    Tzoumas, M

    V. Tzoumas, M. A. Rahimian, G. J. Pappas, and A. Jadbabaie. Minimal actuator placement with bounds on control effort. IEEE Transactions on Control of Network Systems, 3 0 (1): 0 67--78, 2016 c

    2016

  256. [264]

    Tzoumas, N

    V. Tzoumas, N. A. Atanasov, A. Jadbabaie, and G. J. Pappas. Scheduling nonlinear sensors for stochastic process estimation. In A merican C ontrol C onference , pages 580--585, 2017 a

    2017

  257. [265]

    Tzoumas, K

    V. Tzoumas, K. Gatsis, A. Jadbabaie, and G. J. Pappas. Resilient monotone submodular function maximization. In IEEE Conf.\ on Decision and Control , pages 1362--1367, 2017 b

    2017

  258. [266]

    Tzoumas, A

    V. Tzoumas, A. Jadbabaie, and G. J. Pappas. Resilient monotone sequential maximization. In IEEE Conf.\ on Decision and Control , pages 7261--7268, 2018 a

    2018

  259. [267]

    Tzoumas, Y

    V. Tzoumas, Y. Xue, S. Pequito, P. Bogdan, and G. J. Pappas. Selecting sensors in biological fractional-order systems. IEEE Transactions on Control of Network Systems, 5 0 (2): 0 709--721, 2018 b

    2018

  260. [268]

    Tzoumas, L

    V. Tzoumas, L. Carlone, G. J. Pappas, and A. Jadbabaie. Lqg control and sensing co-design. IEEE Transactions on Automatic Control, 66 0 (4): 0 1468--1483, 2021

    2021

  261. [269]

    Tzoumas, A

    V. Tzoumas, A. Jadbabaie, and G. J. Pappas. Robust and adaptive sequential submodular optimization. IEEE Transactions on Automatic Control, 67 0 (1): 0 89--104, 2022

    2022

  262. [270]

    A. K. Umrawal, V. Aggarwal, and C. J. Quinn. Fractional budget allocation for influence maximization. In IEEE Conf.\ on Decision and Control , pages 4327--4332, 2023

    2023

  263. [271]

    Valibeygi and R

    A. Valibeygi and R. A. de Callafon. Cooperative energy scheduling for microgrids under peak demand energy plans. In IEEE Conf.\ on Decision and Control , pages 3110--3115, 2019

    2019

  264. [272]

    Van Over, B

    B. Van Over, B. Li, E. K. Chong, and A. Pezeshki. An improved greedy curvature bound in finite-horizon string optimization with an application to a sensor coverage problem. In IEEE Conf.\ on Decision and Control , pages 1257--1262, 2023

    2023

  265. [273]

    A. Vetta. Nash equilibria in competitive societies, with applications to facility location, traffic routing and auctions. In IEEE Symposium on Foundations of Computer Science, pages 416--425, 2002

    2002

  266. [274]

    Vondr \'a k

    J. Vondr \'a k. Symmetry and approximability of submodular maximization problems. SIAM Journal on Computing, 42 0 (1): 0 265--304, 2013

    2013

  267. [275]

    Wang and L

    J. Wang and L. Shi. Optimal D o S attacks on remote state estimation with a router. In IEEE Conf.\ on Decision and Control , pages 6384--6389, 2018

    2018

  268. [276]

    Welikala, C

    S. Welikala, C. G. Cassandras, H. Lin, and P. J. Antsaklis. A new performance bound for submodular maximization problems and its application to multi-agent optimal coverage problems. Automatica, 144: 0 110493, 2022

    2022

  269. [277]

    R. K. Williams, A. Gasparri, and G. Ulivi. Decentralized matroid optimization for topology constraints in multi-robot allocation problems. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 293--300, 2017

    2017

  270. [278]

    P. Wolf, S. Moura, and M. Krstic. On Optimizing Sensor Placement for Spatio-Temporal Temperature Estimation in Large Battery Packs . In IEEE Conf.\ on Decision and Control , pages 973--978, 2012

    2012

  271. [279]

    S. Wu, X. Ren, Q.-S. Jia, K. H. Johansson, and L. Shi. Learning optimal scheduling policy for remote state estimation under uncertain channel condition. IEEE Transactions on Control of Network Systems, 7 0 (2): 0 579--591, 2020

    2020

  272. [280]

    Y. Xu, H. Xiang, L. Yang, R. Lu, and D. E. Quevedo. Optimal transmission strategy for multiple markovian fading channels: Existence, structure, and approximation. Automatica, 158: 0 111312, 2023 a

    2023

  273. [281]

    Xu and V

    Z. Xu and V. Tzoumas. Resource-aware distributed submodular maximization: A paradigm for multi-robot decision-making. In IEEE Conf.\ on Decision and Control , pages 5959--5966, 2022

    2022

  274. [282]

    Xu and V

    Z. Xu and V. Tzoumas. Performance-aware self-configurable multi-agent networks: A distributed submodular approach for simultaneous coordination and network design. In IEEE Conf.\ on Decision and Control , pages 5393--5400, 2024

    2024

  275. [283]

    Z. Xu, X. Lin, and V. Tzoumas. Bandit submodular maximization for multi-robot coordination in unpredictable and partially observable environments. In Robotics: Science and Systems (RSS), 2023 b

    2023

  276. [284]

    Z. Xu, H. Zhou, and V. Tzoumas. Online submodular coordination with bounded tracking regret: Theory, algorithm, and applications to multi-robot coordination. IEEE Robotics and Automation Letters, 8 0 (4): 0 2261--2268, 2023 c

    2023

  277. [285]

    Z. Xu, X. Lin, and V. Tzoumas. Leveraging untrustworthy commands for multi-robot coordination in unpredictable environments: A bandit submodular maximization approach. In A merican C ontrol C onference , pages 192--199, 2024

    2024

  278. [286]

    Ye and V

    L. Ye and V. Gupta. Client scheduling for federated learning over wireless networks: A submodular optimization approach. In IEEE Conf.\ on Decision and Control , pages 63--68, 2021

    2021

  279. [287]

    Ye and S

    L. Ye and S. Sundaram. Sensor selection for hypothesis testing: Complexity and greedy algorithms. In IEEE Conf.\ on Decision and Control , pages 7844--7849, 2019

    2019

  280. [288]

    Ye and S

    L. Ye and S. Sundaram. Distributed maximization of submodular and approximately submodular functions. In IEEE Conf.\ on Decision and Control , pages 2979--2984, 2020

    2020

  281. [289]

    L. Ye, A. Mitra, and S. Sundaram. Near-optimal data source selection for bayesian learning. In Learning for Dynamics and Control, pages 854--865. PMLR, 2021

    2021

  282. [290]

    Ye, Z.-W

    L. Ye, Z.-W. Liu, M. Chi, and V. Gupta. Maximization of nonsubmodular functions under multiple constraints with applications. Automatica, 155: 0 111126, 2023

    2023

  283. [291]

    Y. Yi, T. Castiglia, and S. Patterson. Shifting opinions in a social network through leader selection. IEEE Transactions on Control of Network Systems, 8 0 (3): 0 1116--1127, 2021

    2021

  284. [292]

    Y. Yi, L. Shan, P. E. Par\'e, and K. H. Johansson. Edge deletion algorithms for minimizing spread in SIR epidemic models. SIAM Journal on Control and Optimization, 60 0 (2): 0 S246--S273, 2022

    2022

  285. [293]

    Zhang, R

    H. Zhang, R. Ayoub, and S. Sundaram. Sensor selection for K alman filtering of linear dynamical systems: Complexity, limitations and greedy algorithms. Automatica, 78: 0 202--210, 2017 a

    2017

  286. [294]

    Zhang, E

    Z. Zhang, E. K. Chong, A. Pezeshki, W. Moran, and S. D. Howard. Submodularity and optimality of fusion rules in balanced binary relay trees. In IEEE Conf.\ on Decision and Control , pages 3802--3807, 2012

    2012

  287. [295]

    Zhang, Z

    Z. Zhang, Z. Wang, E. K. Chong, A. Pezeshki, and W. Moran. Near optimality of greedy strategies for string submodular functions with forward and backward curvature constraints. In IEEE Conf.\ on Decision and Control , pages 5156--5161, 2013

    2013

  288. [296]

    Zhang, E

    Z. Zhang, E. K. Chong, A. Pezeshki, B. Moran, and S. D. Howard. Near-optimal distributed detection in balanced binary relay trees. IEEE Transactions on Control of Network Systems, 4 0 (4): 0 826--837, 2017 b

    2017

  289. [297]

    J. Zhao, Q. Liu, L. Wang, and X. Wang. Relative influence maximization in competitive dynamics on complex networks. In IEEE Conf.\ on Decision and Control , pages 6583--6588, 2015

    2015

  290. [298]

    Zhao and P

    Y. Zhao and P. A. Vela. Good feature matching: Toward accurate, robust vo/vslam with low latency. IEEE Transactions on Robotics, 36 0 (3): 0 657--675, 2020

    2020

  291. [299]

    Zheng and N

    Z. Zheng and N. B. Shroff. Submodular utility maximization for deadline constrained data collection in sensor networks. IEEE Transactions on Automatic Control, 59 0 (9): 0 2400--2412, 2014

    2014

  292. [300]

    Zhou and V

    L. Zhou and V. Kumar. Robust multi-robot active target tracking against sensing and communication attacks. IEEE Transactions on Robotics, 39 0 (3): 0 1768--1780, 2023

    2023

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.