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.

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Researchers
Awards
- AWARD MERL Researchers Win Best Workshop Poster Award at the 2023 IEEE International Conference on Robotics and Automation (ICRA)
  Date: June 2, 2023
  Awarded to: Yuki Shirai, Devesh Jha, Arvind Raghunathan and Dennis Hong
  MERL Contacts: Devesh K. Jha; Arvind Raghunathan; Yuki Shirai
  Research Areas: Artificial Intelligence, Optimization, Robotics
  Brief
  - MERL's paper titled: "Closed-Loop Tactile Controller for Tool Manipulation" Won the Best Poster Award in the workshop on "Embracing contacts : Making robots physically interact with our world". First author and MERL intern, Yuki Shirai, was presented with the award at a ceremony held at ICRA in London. MERL researchers Devesh Jha, Principal Research Scientist, and Arvind Raghunathan, Senior Principal Research Scientist and Senior Team Leader as well as Prof. Dennis Hong of University of California, Los Angeles are also coauthors.
    
    The paper presents a technique to manipulate an object using a tool in a closed-loop fashion using vision-based tactile sensors. More information about the workshop and the various speakers can be found here https://sites.google.com/view/icra2023embracingcontacts/home.
- 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, Optimization
  Brief
  - 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.
- 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 Processing
  Brief
  - 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.
See All Awards for Optimization
News & Events
- NEWS MERL researchers present 7 papers at CDC 2024
  Date: December 16, 2024 - December 19, 2024
  Where: Milan, Italy
  MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; James Queeney; Abraham P. Vinod; Avishai Weiss; Gordon Wichern
  Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
  Brief
  - MERL researchers presented 7 papers at the recently concluded Conference on Decision and Control (CDC) 2024 in Milan, Italy. The papers covered a wide range of topics including safety shielding for stochastic model predictive control, reinforcement learning using expert observations, physics-constrained meta learning for positioning, variational-Bayes Kalman filtering, Bayesian measurement masks for GNSS positioning, divert-feasible lunar landing, and centering and stochastic control using constrained zonotopes.
    
    As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
    
    In addition, Ankush Chakrabarty (Principal Research Scientist, Multiphysical Systems Team) was an invited speaker in the pre-conference Workshop on "Learning Dynamics From Data" where he gave a talk on few-shot meta-learning for black-box identification using data from similar systems.
- NEWS MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
  Date: December 10, 2024 - December 15, 2024
  Where: Advances in Neural Processing Systems (NeurIPS)
  MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
  Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
  Brief
  - MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.
    
    1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530
    
    2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639
    
    3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.
    
    4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?
    
    5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.
    
    6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.
    
    7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.
    
    8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.
    
    9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.
    
    10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.
    
    11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.
    
    12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.
    
    13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.
    
    MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
See All News & Events for Optimization
Research Highlights
- Edge-Assisted Internet of Vehicles for Smart Mobility
Internships
- CA0117: Internship - Feedforward-Feedback Co-Design
  
  MERL is seeking a graduate student to develop a scalable optimization-based framework for feedforward-feedback co-design for nonlinear dynamical systems subject to path constraints. The framework will 1) support modeling and operational uncertainties, and 2) guarantee static and dynamic feasibility in closed-loop. The solution approach will leverage the state-of-the-art in sequential convex programming, contraction analysis, and first-order methods for semidefinite programming. The methods will be evaluated on high-dimensional motion planning problems in robotics. The results of the internship are expected to be published in top-tier conferences and/or journal in robotics, control systems, and optimization.
  The internship is expected to start in Spring or Summer 2025 with an expected duration of 3-6 months depending on the agreed scope and intermediate progress.
  Required Specific Experience
  - Current/Past enrollment in a Ph.D. program in Mechanical, Aerospace, Electrical Engineering, Computer Science, or Applied Mathematics.
  - 2+ years of research in at least some of: first-order algorithms for SDPs, contraction analysis, nonconvex trajectory optimization.
  - Strong programming skills in Python and/or C/C++.
- ST0103: Internship - Data-Driven Control for High-Dimensional Dynamics
  
  MERL is seeking a motivated and qualified individual to work on data-driven estimation and control of high-dimensional dynamical systems, with applications in indoor airflow optimization. The ideal candidate will be a PhD student in engineering or related fields with a solid background in estimation, control, and dynamical systems theory. Preferred skills include knowledge of reinforcement learning, data-driven control, nonlinear control, reduced-order modeling (ROM), and partial differential equations (PDEs). Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.
- CA0118: Internship - Spacecraft Guidance, Navigation, and Control
  
  MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates are PhD students with experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, station keeping, rendezvous, landing, attitude control, orbit control, orbit determination, nonlinear estimation, scheduling problems, and optimization-based control. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible.
  Required Specific Experience
  - Current enrollment in a PhD program in Aerospace, Mechanical, Electrical Engineering, or a related field
  - Strong programming skills in Matlab, Python, and/or C/C++
See All Internships for Optimization
Openings
See All Openings at MERL
Recent Publications
- Vinod, A.P., Safaoui, S., Summers, T., Yoshikawa, N., Di Cairano, S., "Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2024.3433229, Vol. 32, No. 6, pp. 2492-2499, January 2025.
  BibTeX TR2024-136 PDF
  - @article{Vinod2025jan,
  - author = {Vinod, Abraham P. and Safaoui, Sleiman and Summers, Tyler and Yoshikawa, Nobuyuki and Di Cairano, Stefano}},
  - title = {Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning},
  - journal = {IEEE Transactions on Control Systems Technology},
  - year = 2025,
  - volume = 32,
  - number = 6,
  - pages = {2492--2499},
  - month = jan,
  - doi = {10.1109/TCST.2024.3433229},
  - url = {https://www.merl.com/publications/TR2024-136}
  - }
- Shirai, Y., Jha, D.K., Raghunathan, A., Romeres, D., "Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming", Nonlinear Analysis: Hybrid Systems, DOI: 10.1016/j.nahs.2024.101466, Vol. 52, December 2024.
  BibTeX TR2024-008 PDF
  - @article{Shirai2024dec,
  - author = {Shirai, Yuki and Jha, Devesh K. and Raghunathan, Arvind and Romeres, Diego},
  - title = {Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming},
  - journal = {Nonlinear Analysis: Hybrid Systems},
  - year = 2024,
  - volume = 52,
  - month = dec,
  - doi = {10.1016/j.nahs.2024.101466},
  - issn = {1751-570X},
  - url = {https://www.merl.com/publications/TR2024-008}
  - }
- Chakrabarty, A., Deshpande, V.M., Wichern, G., Berntorp, K., "Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications", IEEE Annual Conference on Decision and Control (CDC), December 2024.
  BibTeX TR2024-180 PDF
  - @inproceedings{Chakrabarty2024dec,
  - author = {Chakrabarty, Ankush and Deshpande, Vedang M. and Wichern, Gordon and Berntorp, Karl}},
  - title = {Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications},
  - booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-180}
  - }
- Vinod, A.P., Weiss, A., Di Cairano, S., "Inscribing and separating an ellipsoid and a constrained zonotope: Applications in stochastic control and centering", IEEE Annual Conference on Decision and Control (CDC), December 2024.
  BibTeX TR2024-173 PDF
  - @inproceedings{Vinod2024dec,
  - author = {Vinod, Abraham P. and Weiss, Avishai and Di Cairano, Stefano}},
  - title = {Inscribing and separating an ellipsoid and a constrained zonotope: Applications in stochastic control and centering},
  - booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-173}
  - }
- Tang, W.-T., Chakrabarty, A., Paulson, J.A., "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
  BibTeX TR2024-167 PDF
  - @inproceedings{Tang2024dec,
  - author = {Tang, Wei-Ting and Chakrabarty, Ankush and Paulson, Joel A.}},
  - title = {TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions},
  - booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-167}
  - }
- Xiang, X., Palash, R., Yagyu, E., Dunham, S., Teo, K.H., Chowdhury, N., "AI-assisted Field Plate Design of GaN HEMT Device", Advanced Theory and Simulation, October 2024.
  BibTeX TR2024-152 PDF
  - @article{Xiang2024oct,
  - author = {Xiang, Xiaofeng and Palash, Rafid and Yagyu, Eiji and Dunham, Scott and Teo, Koon Hoo and Chowdhury, Nadim}},
  - title = {AI-assisted Field Plate Design of GaN HEMT Device},
  - journal = {Advanced Theory and Simulation},
  - year = 2024,
  - month = oct,
  - url = {https://www.merl.com/publications/TR2024-152}
  - }
- Sun, H., Kosanic, M., Kawano, S., Raghunathan, A., Kitamura, S., Takaguchi, Y., "Proactive Sequential Phase Swapping Scheduling for Distribution Systems with a Finite Horizon", IEEE PES Asia-Pacific Power and Energy Engineering Conference, October 2024.
  BibTeX TR2024-149 PDF
  - @inproceedings{Sun2024oct,
  - author = {Sun, Hongbo and Kosanic, Miroslav and Kawano, Shunsuke and Raghunathan, Arvind and Kitamura, Shoichi and Takaguchi, Yusuke}},
  - title = {Proactive Sequential Phase Swapping Scheduling for Distribution Systems with a Finite Horizon},
  - booktitle = {IEEE PES Asia-Pacific Power and Energy Engineering Conference},
  - year = 2024,
  - month = oct,
  - url = {https://www.merl.com/publications/TR2024-149}
  - }
- Bortoff, S.A., Laughman, C.R., Deshpande, V.M., Qiao, H., "Fluid Property Functions in Polar and Parabolic Coordinates", American Modelica Conference, October 2024.
  BibTeX TR2024-144 PDF
  - @inproceedings{Bortoff2024oct,
  - author = {Bortoff, Scott A. and Laughman, Christopher R. and Deshpande, Vedang M. and Qiao, Hongtao}},
  - title = {Fluid Property Functions in Polar and Parabolic Coordinates},
  - booktitle = {American Modelica Conference},
  - year = 2024,
  - month = oct,
  - url = {https://www.merl.com/publications/TR2024-144}
  - }
See All Publications for Optimization
Videos

[MERL Seminar Series Fall 2024] AI-assisted Power Grid Dispatch and Control: Optimization, Safety, and Real-world Demonstrations

Description of the abort-safe spacecraft rendezvous example

Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning

[MERL Seminar Series Fall 2023] Co-Design of Complex Systems: From Autonomy to Future Mobility

[MERL Seminar Series Fall 2023] Composable Optimization for Robotic Simulation, Planning, and Control

[MERL Seminar Series Fall 2023] A Process Systems Engineering Perspective on Carbon Capture: Key Challenges and Opportunities

[TRO 2024] Safe Multiagent Motion Planning Under Uncertainty for Drones Using Filtered Reinforcement Learning

Simultaneous Trajectory Optimization and Contact Selection for Multi-Modal Manipulation Planning

[MERL Seminar Series Spring 2023] Learning and Dynamical Systems

Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization

Robot Locomotion by Automated Controller Tuning

Real-time Mixed-integer Programming for Vehicle Decision Making and Motion Planning

Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Networks

[MERL Seminar Series Fall 2022] Design, Identification and Simulation of PM Synchronous Machines for Traction

Toshiaki Koike-Akino Gives Seminar Talk at IEEE Boston Photonics

[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?

[MERL Seminar Series Spring 2022] Extreme optics design as a large-scale optimization problem

[MERL Seminar Series 2021] Integration of Analytics Techniques for Algorithmic Sports Betting

Multiview Sensing with Unknown Permutations: An Optimal Transport Approach

Imaging for inverse scattering in Reflection Tomography

Electric Satellite Station Keeping, Attitude Control, and Momentum Management by MPC

Polar Coding with Chemical Reaction Networks for Molecular Communications

EMI reduction in PWM inverters using adaptive frequency modulated carriers

Fast Pattern Search in Big Data

Five Axis Additive Manufacturing

Globally Optimal Power Flow
Software & Data Downloads