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
Toshiaki
Koike-Akino
Daniel N.
Nikovski
Arvind
Raghunathan
Philip V.
Orlik
Mouhacine
Benosman
Ankush
Chakrabarty
Rien
Quirynen
Kieran
Parsons
Ye
Wang
Christopher R.
Laughman
Karl
Berntorp
Petros T.
Boufounos
Matthew
Brand
Yebin
Wang
Pu
(Perry)
WangScott A.
Bortoff
Hassan
Mansour
Devesh K.
Jha
Jianlin
Guo
Diego
Romeres
Hongbo
Sun
Dehong
Liu
Yanting
Ma
Avishai
Weiss
Marcus
Greiff
Marcel
Menner
Hongtao
Qiao
Jinyun
Zhang
Chungwei
Lin
Saviz
Mowlavi
Gordon
Wichern
William S.
Yerazunis
Jing
Zhang
Wataru
Tsujita
Abraham P.
Vinod
Bingnan
Wang
Jose
Amaya
Anoop
Cherian
Radu
Corcodel
Vedang M.
Deshpande
Abraham
Goldsmith
Joshua
Rapp
Jing
Liu
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Awards
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AWARD Arvind Raghunathan receives Roberto Tempo Best CDC Paper Award at 2022 IEEE Conference on Decision & Control (CDC) Date: December 8, 2022
Awarded to: Arvind Raghunathan
MERL Contact: Arvind Raghunathan
Research Areas: Control, OptimizationBrief- Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).
The award is given annually in honor of Roberto Tempo, the 44th President of the IEEE Control Systems Society (CSS). The Tempo Award Committee selects the best paper from the previous year's CDC based on originality, potential impact on any aspect of control theory, technology, or implementation, and for the clarity of writing. This year's award committee was headed by Prof. Patrizio Colaneri, Politecnico di Milano. Arvind's paper was nominated for the award by Prof. Lorenz Biegler, Carnegie Mellon University, with supporting letters from Prof. Andreas Waechter, Northwestern University, and Prof. Victor Zavala, University of Wisconsin-Madison.
- Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).
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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: 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.
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News & Events
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NEWS Rien Quirynen Appointed IPC Vice-Chair for the 8th IFAC Conference on NMPC 2024 Date: August 27, 2024 - August 30, 2024
Where: Kyoto, Japan
MERL Contact: Rien Quirynen
Research Areas: Control, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researcher Rien Quirynen has been appointed as Vice-Chair from Industry of the International Program Committee of the 8th IFAC Conference on Nonlinear Model Predictive Control, which will be held in Kyoto, Japan, in August 2024.
IFAC NMPC is the main symposium focused on model predictive control, theory, methods and applications, includes contributions on control, optimization, and machine learning research, and is held every 3 years.
- MERL researcher Rien Quirynen has been appointed as Vice-Chair from Industry of the International Program Committee of the 8th IFAC Conference on Nonlinear Model Predictive Control, which will be held in Kyoto, Japan, in August 2024.
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NEWS Yebin Wang delivered an invited industry talk at the 1st IEEE Industrial Electronics Society Annual On-Line Conference Date: December 9, 2022 - December 11, 2022
MERL Contact: Yebin Wang
Research Areas: Communications, Control, OptimizationBrief- Future factory, in the era of industry 4.0, is characterized by autonomy, digital twin, and mass customization. This talk, titled "Future factory automation and cyber-physical system: an industrial perspective," focuses on tackling the challenges arising from mass customization, for example reconfigurable machine controller and material flow.
See All News & Events for Optimization -
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Research Highlights
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Internships
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MS1957: Estimation and Model Structure Identification for Digital Twins
MERL is looking for a highly motivated and qualified candidate to work on estimation and model structure identification for digital twins of multi-physical systems. The research will involve study and development of white-box and grey-box model calibration and identification methods suitable for large-scale systems. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear estimation, model identification, optimization, data-driven and reduced order modeling, and machine learning; with expertise demonstrated via, e.g., peer-reviewed publications. Prior programming experience in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months and start date is flexible.
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CA1940: Autonomous vehicle planning and contro in uncertain environments
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.
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MS2012: Residual Model Learning for Building Energy Systems
MERL is looking for a highly motivated and qualified candidate to work on learning residual dynamics to augment ODE/DAE-based models of building energy systems. The ideal candidate will have a strong understanding of system identification, optimization, machine learning and/or function approximation; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred; experience with Modelica/FMUs is a plus. PhD students are strongly preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible.
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Recent Publications
- "A System Approach for Efficiency Enhancement and Linearization Technique of Dual-Input Doherty Power Amplifier", IEEE Journal of Microwaves, February 2023.BibTeX TR2023-008 PDF
- @article{Kantana2023feb,
- author = {Kantana, Chouabi and Benosman, Mouhacine and Ma, Rui and Komatsuzaki, Y.},
- title = {A System Approach for Efficiency Enhancement and Linearization Technique of Dual-Input Doherty Power Amplifier},
- journal = {IEEE Journal of Microwaves},
- year = 2023,
- month = feb,
- url = {https://www.merl.com/publications/TR2023-008}
- }
, - "Parameter-Adaptive Reference Governors with Learned Robust Constraint-Admissible Sets", Control Engineering Practice, DOI: 10.1016/j.conengprac.2023.105450, Vol. 133, February 2023.BibTeX TR2023-005 PDF
- @article{Chakrabarty2023feb,
- author = {Chakrabarty, Ankush and Berntorp, Karl and Di Cairano, Stefano},
- title = {Parameter-Adaptive Reference Governors with Learned Robust Constraint-Admissible Sets},
- journal = {Control Engineering Practice},
- year = 2023,
- volume = 133,
- month = feb,
- doi = {10.1016/j.conengprac.2023.105450},
- url = {https://www.merl.com/publications/TR2023-005}
- }
, - "Learning a Constrained Optimizer: A Primal Method", AAAI Conference on Artificial Intelligence, January 2023.BibTeX TR2023-003 PDF
- @inproceedings{Liu2023jan,
- author = {Liu, Tao and Cherian, Anoop},
- title = {Learning a Constrained Optimizer: A Primal Method},
- booktitle = {AAAI Conference on Artificial Intelligence},
- year = 2023,
- month = jan,
- url = {https://www.merl.com/publications/TR2023-003}
- }
, - "GSR: A Generalized Symbolic Regression Approach", Transactions on Machine Learning Research, January 2023.BibTeX TR2023-002 PDF
- @article{Tohme2023jan,
- author = {Tohme, Tony and Liu, Dehong and Youcef-Toumi, Kamal},
- title = {GSR: A Generalized Symbolic Regression Approach},
- journal = {Transactions on Machine Learning Research},
- year = 2023,
- month = jan,
- url = {https://www.merl.com/publications/TR2023-002}
- }
, - "Transfer Learning for Bayesian Optimization with Principal Component Analysis", International Conference on Machine Learning and Applications (ICMLA), December 2022.BibTeX TR2022-169 PDF
- @inproceedings{Masui2022dec,
- author = {Masui, Hideyuki and Romeres, Diego and Nikovski, Daniel N.},
- title = {Transfer Learning for Bayesian Optimization with Principal Component Analysis},
- booktitle = {International Conference on Machine Learning and Applications (ICMLA)},
- year = 2022,
- month = dec,
- url = {https://www.merl.com/publications/TR2022-169}
- }
, - "Simulation Failure Robust Bayesian Optimization for Data-Driven Parameter Estimation", IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2022.3216790, December 2022.BibTeX TR2022-168 PDF
- @article{Chakrabarty2022dec2,
- author = {Chakrabarty, Ankush and Bortoff, Scott A. and Laughman, Christopher R.},
- title = {Simulation Failure Robust Bayesian Optimization for Data-Driven Parameter Estimation},
- journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
- year = 2022,
- month = dec,
- doi = {10.1109/TSMC.2022.3216790},
- url = {https://www.merl.com/publications/TR2022-168}
- }
, - "Point Cloud Soft Multicast for Untethered XR Users", IEEE Transactions on Multimedia, December 2022.BibTeX TR2022-164 PDF
- @article{SoushiUeno;Fujihashi2022dec,
- author = {Soushi Ueno and Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi},
- title = {Point Cloud Soft Multicast for Untethered XR Users},
- journal = {IEEE Transactions on Multimedia},
- year = 2022,
- month = dec,
- url = {https://www.merl.com/publications/TR2022-164}
- }
, - "Optimal Control of PDEs Using Physics-Informed Neural Networks", Advances in Neural Information Processing Systems (NeurIPS) workshop, December 2022.BibTeX TR2022-163 PDF
- @inproceedings{Mowlavi2022dec,
- author = {Mowlavi, Saviz and Nabi, Saleh},
- title = {Optimal Control of PDEs Using Physics-Informed Neural Networks},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS) workshop},
- year = 2022,
- month = dec,
- url = {https://www.merl.com/publications/TR2022-163}
- }
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- "A System Approach for Efficiency Enhancement and Linearization Technique of Dual-Input Doherty Power Amplifier", IEEE Journal of Microwaves, February 2023.
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Videos
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Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization
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Robot Locomotion by Automated Controller Tuning
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Real-time Mixed-integer Programming for Vehicle Decision Making and Motion Planning
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Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Networks
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[MERL Seminar Series Fall 2022] Design, Identification and Simulation of PM Synchronous Machines for Traction
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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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Fast Pattern Search in Big Data
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Five Axis Additive Manufacturing
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Globally Optimal Power Flow
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Software Downloads