Optimization
Efficient solutions to large-scale problems.
Much of MERL's research activity involves formulating scientific and engineering problems as optimizations, which can be solved in an efficient way. We have developed fundamental algorithms to better solve classic problems, such as quadratic programs and minimum-cost paths. Our work also involves developing theoretical bounds to understand performance limits.
Quick Links
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Researchers
Stefano
Di Cairano
Daniel N.
Nikovski
Toshiaki
Koike-Akino
Arvind
Raghunathan
Philip V.
Orlik
Rien
Quirynen
Mouhacine
Benosman
Ankush
Chakrabarty
Ye
Wang
Christopher R.
Laughman
Kieran
Parsons
Matthew E.
Brand
Petros T.
Boufounos
Karl
Berntorp
Hassan
Mansour
Yebin
Wang
Scott A.
Bortoff
Devesh K.
Jha
Jianlin
Guo
Saleh
Nabi
Pu
(Perry)
WangHongbo
Sun
Kyeong Jin
(K.J.)
KimDiego
Romeres
Dehong
Liu
Yanting
Ma
Hongtao
Qiao
Rui
Ma
Avishai
Weiss
Jinyun
Zhang
Chungwei
Lin
William S.
Yerazunis
Marcus
Greiff
Marcel
Menner
Abraham P.
Vinod
Gordon
Wichern
Jose
Amaya
Abraham M.
Goldsmith
Jay
Thornton
Bingnan
Wang
Jing
Zhang
Joshua
Rapp
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Awards
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AWARD Outstanding Presentation Award at the 28th Conference of Information Processing Society of Japan/Consumer Device & Systems Date: October 20, 2020
Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip V. Orlik
Research Areas: Communications, Optimization, Signal ProcessingBrief- MELCO and MERL researchers have won "Outstanding Presentation Award" at 28th Conference of Information Processing Society of Japan (IPSJ)/Consumer Device & Systems held on September 29-30, 2020. The paper titled "IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands" reports IEEE 802.19.3 standard development on coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. MERL and MELCO have been leading this standard development and made major technical contributions, which propose methods to mitigate interference in smart meter systems. The authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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AWARD Best conference paper of IEEE PES-GM 2020 Date: June 18, 2020
Awarded to: Tong Huang, Hongbo Sun, K.J. Kim, Daniel Nikovski, Le Xie
MERL Contacts: Kyeong Jin (K.J.) Kim; Daniel N. Nikovski; Hongbo Sun
Research Areas: Data Analytics, Electric Systems, OptimizationBrief- A paper on A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Natural Disasters, written by Tong Huang, a former MERL intern from Texas A&M University, has been selected as one of the Best Conference Papers at the 2020 Power and Energy Society General Meeting (PES-GM). IEEE PES-GM is the flagship conference for the IEEE Power and Energy Society. The work was done in collaboration with Hongbo Sun, K. J. Kim, and Daniel Nikovski from MERL, and Tong's advisor, Prof. Le Xie from Texas A&M University.
See All Awards for MERL -
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News & Events
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NEWS MERL researchers win ASME Energy Systems Technical Committee Best Paper Award at 2022 American Control Conference Date: June 8, 2022
Where: 2022 American Control Conference
MERL Contacts: Ankush Chakrabarty; Christopher R. Laughman
Research Areas: Control, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Researchers from EPFL (Wenjie Xu, Colin Jones) and EMPA (Bratislav Svetozarevic), in collaboration with MERL researchers Ankush Chakrabarty and Chris Laughman, recently won the ASME Energy Systems Technical Committee Best Paper Award at the 2022 American Control Conference for their work on "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Performance Optimization with Unmodeled Constraints" out of 19 nominations and 3 finalists. The paper describes a data-driven framework for optimizing the performance of constrained control systems by systematically re-evaluating how cautiously/aggressively one should explore the search space to avoid sustained, large-magnitude constraint violations while tolerating small violations, and demonstrates these methods on a physics-based model of a vapor compression cycle.
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NEWS MERL researchers presented 9 papers at the American Control Conference (ACC) Date: June 8, 2022 - June 10, 2022
Where: Atlanta, GA
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Marcel Menner; Rien Quirynen; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.
See All News & Events for Optimization -
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Research Highlights
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Internships
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CA1742: Mixed-Integer Programming for Motion Planning and Control
MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, motion planning and control of hybrid systems. The research will involve the study and development of numerical optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: branch-and-bound type methods, heuristics for mixed-integer programming (pre-solve, cutting planes, warm starting, integer-feasible solutions), modeling and formulation of MIPs for hybrid control systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on mixed-integer programming or numerical optimization, are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.
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CA1728: Safe data-driven control of dynamical systems under uncertainty
MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety and performance in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (robust and stochastic) model predictive control, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in submission of relevant results to peer-reviewed conference proceedings and journals, and development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is Summer 2022.
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CA1706: Perception-aware vehicle control
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control algorithms accounting for perception of the uncertain surrounding environment. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible of: predictive control algorithms for linear and nonlinear systems, stochastic constrained control, e.g., chance constraints, stochastic optimization, statistical estimation, perception system modeling, and vehicle modeling and control. Good programming skills in MATLAB, Python or C/C++ are required. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.
See All Internships for Optimization -
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Recent Publications
- "Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving", American Control Conference (ACC), June 2022.BibTeX TR2022-062 PDF
- @inproceedings{Bonzanini2022jun,
- author = {Bonzanini, Angelo Domenico and Mesbah, Ali and Di Cairano, Stefano},
- title = {Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-062}
- }
, - "Constrained Smoothers for State Estimation of Vapor Compression Cycles", American Control Conference (ACC), June 2022.BibTeX TR2022-063 PDF
- @inproceedings{Deshpande2022jun,
- author = {Deshpande, Vedang and Laughman, Christopher R. and Ma, Yingbo and Rackauckas, Chris},
- title = {Constrained Smoothers for State Estimation of Vapor Compression Cycles},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-063}
- }
, - "Local Eigenmotion Control for Near Rectilinear Halo Orbits", American Control Conference (ACC), June 2022.BibTeX TR2022-060 PDF
- @inproceedings{Elango2022jun,
- author = {Elango, Purnanand and Di Cairano, Stefano and Kalabic, Uros and Weiss, Avishai},
- title = {Local Eigenmotion Control for Near Rectilinear Halo Orbits},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-060}
- }
, - "Coordination of Autonomous Vehicles and Dynamic Traffic Rules in Mixed Automated/Manual Traffic", American Control Conference (ACC), June 2022.BibTeX TR2022-059 PDF
- @inproceedings{Firoozi2022jun,
- author = {Firoozi, Roya and Quirynen, Rien and Di Cairano, Stefano},
- title = {Coordination of Autonomous Vehicles and Dynamic Traffic Rules in Mixed Automated/Manual Traffic},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-059}
- }
, - "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints", American Control Conference (ACC), June 2022, pp. 5288-5293.BibTeX TR2022-064 PDF
- @inproceedings{Xu2022jun,
- author = {Xu, Wenjie and Jones, Colin and Svetozarevic, Bratislav and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- pages = {5288--5293},
- month = jun,
- isbn = {978-1-6654-5197-0},
- url = {https://www.merl.com/publications/TR2022-064}
- }
, - "Robust Model Predictive Control with Data-Driven Koopman Operators", American Control Conference (ACC), June 2022.BibTeX TR2022-054 PDF Video
- @inproceedings{Mamakoukas2022jun,
- author = {Mamakoukas, Giorgos and Di Cairano, Stefano and Vinod, Abraham P.},
- title = {Robust Model Predictive Control with Data-Driven Koopman Operators},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-054}
- }
, - "Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning", IEEE International Conference on Robotics and Automation (ICRA), May 2022.BibTeX TR2022-056 PDF
- @inproceedings{Leu2022may,
- author = {Leu, Jessica and Wang, Yebin and Tomizuka, Masayoshi and Di Cairano, Stefano and },
- title = {Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-056}
- }
, - "Safe multi-agent motion planning via filtered reinforcement learning", IEEE International Conference on Robotics and Automation (ICRA), May 2022.BibTeX TR2022-053 PDF Video
- @inproceedings{Vinod2022may,
- author = {Vinod, Abraham P. and Safaoui, Sleiman and Chakrabarty, Ankush and Quirynen, Rien and Yoshikawa, Nobuyuki and Di Cairano, Stefano},
- title = {Safe multi-agent motion planning via filtered reinforcement learning},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-053}
- }
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- "Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving", American Control Conference (ACC), June 2022.
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Videos
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Toshiaki Koike-Akino Gives Seminar Talk at IEEE Boston Photonics
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[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?
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[MERL Seminar Series Spring 2022] Extreme optics design as a large-scale optimization problem
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[MERL Seminar Series 2021] Integration of Analytics Techniques for Algorithmic Sports Betting
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Multiview Sensing with Unknown Permutations: An Optimal Transport Approach
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Imaging for inverse scattering in Reflection Tomography
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Electric Satellite Station Keeping, Attitude Control, and Momentum Management by MPC
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Polar Coding with Chemical Reaction Networks for Molecular Communications
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EMI reduction in PWM inverters using adaptive frequency modulated carriers
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Globally Optimal Power Flow
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Five Axis Additive Manufacturing
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Fast Pattern Search in Big Data
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Software Downloads