Topics

Optimization Over Networks

Optimization over networks appears in two complementary ways in our research. First, distributed algorithms allow agents to solve a shared optimization or decision-making problem using only local communication. Second, optimization provides a language for analyzing complex networked dynamical systems, including passivity-based control, clustering, sparse design, and cooperative seeking.

This page focuses on the optimization layer itself. Energy dispatch, passivity, and network identification papers are included when their main contribution is also tagged as optimization.

Distributed Negotiation and Decision Making

Distributed negotiation problems model agents that must agree on a decision while respecting local preferences, dynamics, and communication constraints. The resulting algorithms blend optimization, agreement protocols, and feedback interconnections.

Our work studies finite-time dual methods, switching communication, real-time preference-based agreement, and negotiation with connectivity constraints.

Distributed team coordination through optimization
Distributed decision making and cooperative optimization over a network.

Representative Publications:

  1. Y. Ben Shoushan and D. Zelazo, “Negotiation Between Dynamical Systems with Connectivity Constraints,” in 57th Israel Annual Conference on Aerospace Sciences , Tel-Aviv, Israel, Feb. 2017.
    Shoushan2017.pdf Shoushan2017.bibtex
  2. Y. Ben-Shoushan, “Negotiation between Dynamical Systems with Connectivity Constraints,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2017.
    BenShoushan2017.pdf BenShoushan2017.bibtex
  3. D. Zelazo, M. Bürger, and F. Allgöwer, “A Finite-Time Dual Method for Negotiation between Dynamical Systems,” SIAM Journal on Control and Optimization, 51(1):172–194, 2013.
    Zelazo2013_J.pdf DOI: 10.1137/100819175 Zelazo2013_J.bibtex
  4. D. Zelazo, M. Bürger, and F. Allgöwer, “Dynamic Negotiation Under Switching Communication,” in Mathematical System Theory – Festschrift in Honor of Uwe Helmke on the Occasion of his Sixtieth Birthday, K. Hüper and J. Trumpf, Eds. CreateSpace, 2013, p. 479500.
    URL: http://users.cecs.anu.edu.au/ trumpf/UH60Festschrift.pdf Zelazo2012b.bibtex
  5. D. Zelazo, M. Bürger, and F. Allgöwer, “A Distributed Real-Time Algorithm for Preference-Based Agreement,” in Proc. 18th IFAC World Congress, Milan, Italy, Aug. 2011.
    Zelazo2011.pdf DOI: 10.3182/20110828-6-IT-1002.03155 Zelazo2011.bibtex

Network Design, Clustering, and Sparsity

Many networked-control problems ask for a graph, weighting, or sparsity pattern that optimizes performance while preserving structural properties. These questions connect graph theory, convex optimization, and dynamical-systems analysis.

Our work studies sparse relative sensing networks, cycle design, clustering in dynamical networks, and optimization-based graph synthesis.

Optimization-based network design
Network design problems connect graph structure to closed-loop performance.

Representative Publications:

  1. S. Schuler, D. Zelazo, and F. Allgöwer, “Robust Design of Sparse Relative Sensing Networks,” in European Control Conference, Zürich, Switzerland, Jul. 2013.
    Schuler2013.pdf Schuler2013.slides DOI: 10.23919/ecc.2013.6669618 Schuler2013.bibtex
  2. M. Bürger, D. Zelazo, and F. Allgöwer, “Hierarchical Clustering of Dynamical Networks Using a Saddle-Point Analysis,” IEEE Transactions on Automatic Control, 58(1):113–124, 2013.
    Burger2011_J.pdf DOI: 10.1109/TAC.2012.2206695 Burger2011_J.bibtex
  3. S. Schuler, D. Zelazo, and F. Allgöwer, “Design of sparse relative sensing networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, Dec. 2012.
    Schuler2012.pdf Schuler2012.slides DOI: 10.1109/CDC.2012.6426358 Schuler2012.bibtex
  4. M. Bürger, D. Zelazo, and F. Allgöwer, “Combinatorial Insights and Robustness Analysis for Clustering in Dynamic Networks,” in American Control Conference, Montreal, Canada, Jul. 2012.
    Burger2012.pdf DOI: 10.1109/acc.2012.6314935 Burger2012.bibtex
  5. D. Zelazo, S. Schuler, and F. Allgöwer, “Cycles and Sparse Design of Consensus Networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, 2012.
    Zelazo2012d.pdf Zelazo2012d.slides DOI: 10.1109/cdc.2012.6426450 Zelazo2012d.bibtex
  6. M. Bürger, D. Zelazo, and F. Allgöwer, “Network clustering: A dynamical systems and saddle-point perspective,” in 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, Dec. 2011.
    Zelazo2011b.pdf DOI: 10.1109/CDC.2011.6161045 Zelazo2011b.bibtex

Control Synthesis and Cooperative Seeking

Optimization also provides synthesis tools for cooperative control. In passivity-based settings, network optimization gives a constructive route to controller design. In source-seeking and zeroth-order settings, agents use local measurements to optimize an objective that is not available in closed form.

This thread includes optimization-based cooperative control synthesis, networked zeroth-order oracles, moving-source seeking, and energy-dispatch problems with device constraints.

Optimization structure for network inference and control synthesis
Optimization structure arising in network inference and cooperative control synthesis.

Representative Publications:

  1. E. Michael, C. Manzie, T. A. Wood, D. Zelazo, and I. Shames, “Gradient Free Cooperative Seeking of a Moving Source,” Automatica, 152:110948, 2023.
    Michael2022a_J.pdf DOI: 10.1016/j.automatica.2023.110948 Michael2022a_J.bibtex
  2. M. Sharf, I. Romm, M. Palman, D. Zelazo, and B. Cukurel, “Economic Dispatch of a Single Micro-Gas Turbine Under CHP Operation with Uncertain Demands,” Applied Energy, 309:118391, 2022.
    Sharf2021b_J.pdf DOI: 10.1016/j.apenergy.2021.118391 Sharf2021b_J.bibtex
  3. E. Michael, D. Zelazo, T. A. Wood, C. Manzie, and I. Shames, “Optimization with Networked Zeroth-Order Oracles,” in IEEE Conference on Decision and Control, Jeju Island, South Korea, Dec. 2020.
    Michael2020a.pdf DOI: 10.1109/CDC42340.2020.9304272 Michael2020a.bibtex
  4. M. Sharf and D. Zelazo, “Analysis and Synthesis of MIMO Multi-Agent Systems Using Network Optimization,” IEEE Transactions on Automatic Control, 64(11):1558–2523, 2019.
    Sharf2017b_J.pdf DOI: 10.1109/tac.2019.2908258 Sharf2017b_J.bibtex
  5. M. Sharf and D. Zelazo, “Symmetry-Induced Clustering in Multi-Agent Systems using Network Optimization and Passivity,” in 27th Mediterranean Conference on Control and Automation, Akko, Israel, Jul. 2019.
    Sharf2019a.pdf Sharf2019a.slides DOI: 10.1109/med.2019.8798507 Sharf2019a.bibtex
  6. A. Jain, M. Sharf, and D. Zelazo, “Regularization and Feedback Passivation in Cooperative Control of Passivity-Short Systems: A Network Optimization Perspective,” IEEE Control Systems Letters, 2(4):731–736, 2018.
    Jain2018a_J.pdf DOI: 10.1109/lcsys.2018.2847738 Jain2018a_J.bibtex
  7. M. Sharf and D. Zelazo, “A Network Optimization Approach to Cooperative Control Synthesis,” IEEE Control Systems Letters, 1(1):86–91, 2017.
    Sharf2017a_J.pdf DOI: 10.1109/lcsys.2017.2706948 Sharf2017a_J.bibtex
  8. J. F. Rist, M. F. Dias, M. Palman, D. Zelazo, and B. Cukurel, “Economic Dispatch of a Single Micro-Gas Turbine Under CHP Operation,” Applied Energy, 200:1–18, 2017.
    Rist2016_J.pdf DOI: 10.1016/j.apenergy.2017.05.064 Rist2016_J.bibtex

Related Publications:

  1. E. Michael, C. Manzie, T. A. Wood, D. Zelazo, and I. Shames, “Gradient Free Cooperative Seeking of a Moving Source,” Automatica, 152:110948, 2023.
    Michael2022a_J.pdf DOI: 10.1016/j.automatica.2023.110948 Michael2022a_J.bibtex
  2. M. Sharf, I. Romm, M. Palman, D. Zelazo, and B. Cukurel, “Economic Dispatch of a Single Micro-Gas Turbine Under CHP Operation with Uncertain Demands,” Applied Energy, 309:118391, 2022.
    Sharf2021b_J.pdf DOI: 10.1016/j.apenergy.2021.118391 Sharf2021b_J.bibtex
  3. N. Peleg, “Economic Dispatch for a Network of Micro-Gas Turbines,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2022.
    Peleg2022.pdf Peleg2022.bibtex
  4. E. Michael, D. Zelazo, T. A. Wood, C. Manzie, and I. Shames, “Optimization with Networked Zeroth-Order Oracles,” in IEEE Conference on Decision and Control, Jeju Island, South Korea, Dec. 2020.
    Michael2020a.pdf DOI: 10.1109/CDC42340.2020.9304272 Michael2020a.bibtex
  5. M. Sharf, “Network Optimization Methods in Passivity-Based Cooperative Control,” phdthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2020.
    Sharf2020.pdf Sharf2020.bibtex
  6. T. Ikeda, D. Zelazo, and K. Kashima, “Maximum Hands-Off Distributed Bearing-Based Formation Control,” in IEEE Conference on Decision and Control, Nice, France, Dec. 2019.
    Ikeda2019a.pdf DOI: 10.1109/cdc40024.2019.9029574 Ikeda2019a.bibtex
  7. M. Sharf and D. Zelazo, “Analysis and Synthesis of MIMO Multi-Agent Systems Using Network Optimization,” IEEE Transactions on Automatic Control, 64(11):1558–2523, 2019.
    Sharf2017b_J.pdf DOI: 10.1109/tac.2019.2908258 Sharf2017b_J.bibtex
  8. M. Sharf and D. Zelazo, “Symmetry-Induced Clustering in Multi-Agent Systems using Network Optimization and Passivity,” in 27th Mediterranean Conference on Control and Automation, Akko, Israel, Jul. 2019.
    Sharf2019a.pdf Sharf2019a.slides DOI: 10.1109/med.2019.8798507 Sharf2019a.bibtex
  9. A. Jain, M. Sharf, and D. Zelazo, “Regularization and Feedback Passivation in Cooperative Control of Passivity-Short Systems: A Network Optimization Perspective,” IEEE Control Systems Letters, 2(4):731–736, 2018.
    Jain2018a_J.pdf DOI: 10.1109/lcsys.2018.2847738 Jain2018a_J.bibtex
  10. M. Sharf and D. Zelazo, “A Network Optimization Approach to Cooperative Control Synthesis,” IEEE Control Systems Letters, 1(1):86–91, 2017.
    Sharf2017a_J.pdf DOI: 10.1109/lcsys.2017.2706948 Sharf2017a_J.bibtex
  11. J. F. Rist, M. F. Dias, M. Palman, D. Zelazo, and B. Cukurel, “Economic Dispatch of a Single Micro-Gas Turbine Under CHP Operation,” Applied Energy, 200:1–18, 2017.
    Rist2016_J.pdf DOI: 10.1016/j.apenergy.2017.05.064 Rist2016_J.bibtex
  12. M. Sharf and D. Zelazo, “On Certain Properties of Convex Functions,” Mar. 2017.
    Sharf2017arxiv.pdf arXiv: https://arxiv.org/abs/1703.00867 Sharf2017arxiv.bibtex
  13. Y. Ben Shoushan and D. Zelazo, “Negotiation Between Dynamical Systems with Connectivity Constraints,” in 57th Israel Annual Conference on Aerospace Sciences , Tel-Aviv, Israel, Feb. 2017.
    Shoushan2017.pdf Shoushan2017.bibtex
  14. Y. Ben-Shoushan, “Negotiation between Dynamical Systems with Connectivity Constraints,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2017.
    BenShoushan2017.pdf BenShoushan2017.bibtex
  15. M. Bürger, D. Zelazo, and F. Allgöwer, “On the Steady-State Inverse-Optimality of Passivity-Based Cooperative Control,” in 4th IFAC Workshop on Distributed Estimation and Control in Networked System, Koblenz, Germany, Sep. 2013.
    Mathias2013.pdf DOI: 10.3182/20130925-2-DE-4044.00004 Mathias2013.bibtex
  16. S. Schuler, D. Zelazo, and F. Allgöwer, “Robust Design of Sparse Relative Sensing Networks,” in European Control Conference, Zürich, Switzerland, Jul. 2013.
    Schuler2013.pdf Schuler2013.slides DOI: 10.23919/ecc.2013.6669618 Schuler2013.bibtex
  17. M. Bürger, D. Zelazo, and F. Allgöwer, “Hierarchical Clustering of Dynamical Networks Using a Saddle-Point Analysis,” IEEE Transactions on Automatic Control, 58(1):113–124, 2013.
    Burger2011_J.pdf DOI: 10.1109/TAC.2012.2206695 Burger2011_J.bibtex
  18. D. Zelazo, M. Bürger, and F. Allgöwer, “A Finite-Time Dual Method for Negotiation between Dynamical Systems,” SIAM Journal on Control and Optimization, 51(1):172–194, 2013.
    Zelazo2013_J.pdf DOI: 10.1137/100819175 Zelazo2013_J.bibtex
  19. D. Zelazo, M. Bürger, and F. Allgöwer, “Dynamic Negotiation Under Switching Communication,” in Mathematical System Theory – Festschrift in Honor of Uwe Helmke on the Occasion of his Sixtieth Birthday, K. Hüper and J. Trumpf, Eds. CreateSpace, 2013, p. 479500.
    URL: http://users.cecs.anu.edu.au/ trumpf/UH60Festschrift.pdf Zelazo2012b.bibtex
  20. S. Schuler, D. Zelazo, and F. Allgöwer, “Design of sparse relative sensing networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, Dec. 2012.
    Schuler2012.pdf Schuler2012.slides DOI: 10.1109/CDC.2012.6426358 Schuler2012.bibtex
  21. M. Bürger, D. Zelazo, and F. Allgöwer, “Combinatorial Insights and Robustness Analysis for Clustering in Dynamic Networks,” in American Control Conference, Montreal, Canada, Jul. 2012.
    Burger2012.pdf DOI: 10.1109/acc.2012.6314935 Burger2012.bibtex
  22. D. Zelazo, R. Dai, and M. Mesbahi, “An energy management system for off-grid power systems,” Energy Systems, 3(2):153–179, 2012.
    Zelazo2011a_J.pdf DOI: 10.1007/s12667-012-0050-4 Zelazo2011a_J.bibtex
  23. D. Zelazo, S. Schuler, and F. Allgöwer, “Cycles and Sparse Design of Consensus Networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, 2012.
    Zelazo2012d.pdf Zelazo2012d.slides DOI: 10.1109/cdc.2012.6426450 Zelazo2012d.bibtex
  24. M. Bürger, D. Zelazo, and F. Allgöwer, “Network clustering: A dynamical systems and saddle-point perspective,” in 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, Dec. 2011.
    Zelazo2011b.pdf DOI: 10.1109/CDC.2011.6161045 Zelazo2011b.bibtex
  25. D. Zelazo, M. Bürger, and F. Allgöwer, “A Distributed Real-Time Algorithm for Preference-Based Agreement,” in Proc. 18th IFAC World Congress, Milan, Italy, Aug. 2011.
    Zelazo2011.pdf DOI: 10.3182/20110828-6-IT-1002.03155 Zelazo2011.bibtex