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
Ankush
Chakrabarty
Arvind
Raghunathan
Toshiaki
Koike-Akino
Daniel N.
Nikovski
Christopher R.
Laughman
Philip V.
Orlik
Yebin
Wang
Ye
Wang
Kieran
Parsons
Devesh K.
Jha
Abraham P.
Vinod
Scott A.
Bortoff
Diego
Romeres
Matthew
Brand
Petros T.
Boufounos
Hassan
Mansour
Pu
(Perry)
WangAvishai
Weiss
Jianlin
Guo
Hongbo
Sun
Vedang M.
Deshpande
Dehong
Liu
Hongtao
Qiao
Yanting
Ma
Saviz
Mowlavi
Yuki
Shirai
Bingnan
Wang
Gordon
Wichern
Purnanand
Elango
Chungwei
Lin
William S.
Yerazunis
Jinyun
Zhang
Abraham
Goldsmith
Shingo
Kobori
Wataru
Tsujita
Anoop
Cherian
Radu
Corcodel
Pedro
Miraldo
Joshua
Rapp
Alexander
Schperberg
Kenji
Inomata
Jing
Liu
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Awards
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AWARD Mitsubishi Electric Team Wins Awards at GalFer Contest Date: June 23, 2025
Awarded to: Bingnan Wang, Tatsuya Yamamoto, Yusuke Sakamoto, Siyuan Sun, Toshiaki Koike-Akino, and Ye Wang
MERL Contacts: Toshiaki Koike-Akino; Bingnan Wang; Ye Wang
Research Areas: Machine Learning, Multi-Physical Modeling, OptimizationBrief- The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.
The GalFer (Galileo Ferraris) contest aims to compare the accuracy and efficiency of data-driven methodologies for the multi-physics simulation of traction electric machines. A total of 26 teams worldwide participated in the contest, which consists of three categories. The MELSUR team, including MERL staff Bingnan Wang, Toshiaki Koike-Akino, Ye Wang, MERL intern Siyuan Sun, Mitsubishi Electric researchers Tatsuya Yamamoto and Yusuke Sakamoto, ranked first for the category of "Novelty" and "Interpolation". The results were announced during an award ceremony at the COMPUMAG 2025 conference in Naples, Italy.
- The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.
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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, RoboticsBrief- 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.
- 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.
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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).
See All Awards for Optimization -
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News & Events
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NEWS MERL contributes to 2025 European Control Conference Date: June 24, 2025 - June 27, 2025
Where: Thessaloniki
MERL Contacts: Stefano Di Cairano; Daniel N. Nikovski; Diego Romeres; Yebin Wang
Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers contributed to both the technical program and workshop organization at the 2025 European Control Conference (ECC), held in Thessaloniki, Greece, from June 24 to 27. ECC is one of the premier conferences in the field of control.
In the main conference, MERL researchers presented four papers covering a range of topics, including: Representation learning, Motion planning for tractor-trailers, Motion planning for mobile manipulators, Learning high-dimensional dynamical systems, Model learning for robotics.
Additionally, MERL co-organized a workshop with the University of Padua titled “Reinforcement Learning for Robotic Control: Recent Developments and Open Challenges.” MERL researcher Diego Romeres also delivered an invited talk titled “Human-Robot Collaborative Assembly” in that workshop.
- MERL researchers contributed to both the technical program and workshop organization at the 2025 European Control Conference (ECC), held in Thessaloniki, Greece, from June 24 to 27. ECC is one of the premier conferences in the field of control.
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TALK [MERL Seminar Series 2025] Behçet Açıkmeşe presents talk titled Robust Trajectory Planning and Control Date & Time: Wednesday, June 25, 2025; 12:00 PM
Speaker: Behçet Açıkmeşe, University of Washington
MERL Host: Avishai Weiss
Research Areas: Control, Dynamical Systems, OptimizationAbstractNext-generation aerospace systems – from asteroid-mining robots and spacecraft swarms to hypersonic vehicles and urban air mobility – demand autonomy that transcends current limits. These missions require spacecraft to operate safely, efficiently, and decisively in unpredictable environments, where every decision must balance performance, resource constraints, and risk. The core challenge lies in solving complex optimal control problems in real time while: i) Exploiting full system capabilities without violating safety limits, ii) Certifying algorithmic reliability for critical Guidance, Navigation, & Control (GN&C) systems, iii) Proving robustness in the presence of uncertainty. Our solution is optimization-based control. By transforming GN&C challenges into structured optimization problems and applying methods of convexification, we achieve provably robust, computationally tractable solutions.
See All News & Events for Optimization -
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Research Highlights
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Internships
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CA0165: Internship - Optimization of Aerial Robot Coordination
MERL is seeking a self-motivated and qualified individual to work on developing an integer/mixed-integer programming solver customarily designed for coordination planning of aerial drones. The ideal candidate will be a PhD student in computer science, mathematics, industrial engineering, or a related discipline, with a solid background in integer optimization. Preferred skills include knowledge of branch-price-and-cut algorithm or column generation, and hands-on experience with callbacks of the Gurobi Optimizer; strong programming skills and experience with at least one of Python, Julia, C/C++, Matlab are also expected. Publication of results produced during the internship is desired. The expected start date is in Fall 2025 or Spring 2026, for a duration of 3- months.
Required Specific Experience
- Significant hands-on experience with integer optimization.
- Experience with trajectory optimization is a plus.
- Fluency in at least one of: Python, Julia, C/C++, Matlab
- Completed their MS, or >30% of their PhD program
- Significant hands-on experience with integer optimization.
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MS0156: Internship - Stochastic Model Predictive Control with Generative Models for Smart Building Control
MERL is looking for a research intern to develop efficient transformer-informed stochastic MPC to control net-zero energy buildings. This is an exciting opportunity to make a real impact in the field of cutting-edge deep learning and predictive control on a real system. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.
The Ideal Candidate Will Have:
- Significant hands-on experience with stochastic MPC
- Publications in SMPC are a strong plus
- Fluency in Python and PyTorch
- Understanding of probabilistic time-series prediction
- Experience with convex programming
- Convex formulations of MPC/SMPC problems are a strong plus
- Completed their MS, or >50% of their PhD program
Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL’s employees to perform their job duties may result in discipline up to and including discharge.
Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.
- Significant hands-on experience with stochastic MPC
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CA0166: Internship - Spacecraft Guidance, Navigation, and Control
MERL is seeking a highly motivated graduate student for a research position in guidance, navigation, and control of spacecraft. The ideal candidate is a PhD student with strong experience in trajectory generation and sequential convex optimization, stochastic optimal control and state estimation, and astrodynamics and the three-body problem. Publication of results produced during the internship is expected. The expected duration of the internship is 3-6 months with a flexible start date.
Required Specific Experience
- Current enrollment in a PhD program in Aerospace, Mechanical, Electrical Engineering, or a related field
- Familiarity with convex optimization solvers
- Strong programming skills in Matlab, Python, and/or C/C++
See All Internships for Optimization -
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Openings
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EA0042: Research Scientist - Control & Learning
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CI0130: Postdoctoral Research Fellow - Artificial General Intelligence (AGI)
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CA0093: Research Scientist - Control for Autonomous Systems
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OR0052: Research Scientist - Optimization Algorithms
See All Openings at MERL -
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Recent Publications
- "Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering", American Control Conference (ACC), July 2025.BibTeX TR2025-106 PDF
- @inproceedings{Zhang2025jul2,
- author = {Zhang, Qi and Avraamidou, Styliani and Paulson, Joel A. and Thakkar, Vyom and Wang, Zhenyu and Chiang, Leo and Braun, Birgit and Rathi, Tushar and Chakrabarty, Ankush and Sorouifar, Farshud and Tang, Wei-Ting and Guertin, France and Munoz, Paola and Sampat, Apoorva},
- title = {{Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-106}
- }
, - "Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints", American Control Conference (ACC), July 2025.BibTeX TR2025-103 PDF
- @inproceedings{Cardona2025jul,
- author = {Cardona, Gustavo and Vasile, Cristian-Ioan and {Di Cairano}, Stefano},
- title = {{Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-103}
- }
, - "Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning", American Control Conference (ACC), July 2025.BibTeX TR2025-104 PDF
- @inproceedings{ChavezArmijos2025jul,
- author = {Chavez Armijos, Andres and Berntorp, Karl and {Di Cairano}, Stefano},
- title = {{Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-104}
- }
, - "Geostationary Satellite Station Keeping and Collocation under High-Thrust Impulsive Control", American Control Conference (ACC), July 2025.BibTeX TR2025-101 PDF
- @inproceedings{Pavlasek2025jul,
- author = {Pavlasek, Natalia and {Di Cairano}, Stefano and Weiss, Avishai},
- title = {{Geostationary Satellite Station Keeping and Collocation under High-Thrust Impulsive Control}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-101}
- }
, - "Station-Keeping on Near-Rectilinear Halo Orbits via Full-State Targeting Model Predictive Control", American Control Conference (ACC), July 2025.BibTeX TR2025-100 PDF
- @inproceedings{Shimane2025jul,
- author = {Shimane, Yuri and {Di Cairano}, Stefano and Ho, Koki and Weiss, Avishai},
- title = {{Station-Keeping on Near-Rectilinear Halo Orbits via Full-State Targeting Model Predictive Control}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-100}
- }
, - "Recursive McCormick Linearization of Multilinear Programs", INFORMS J Computing, June 2025.BibTeX TR2025-098 PDF
- @article{Raghunathan2025jun,
- author = {Raghunathan, Arvind and Cardonha, Carlos and Bergman, David and Nohra, Carlos J.},
- title = {{Recursive McCormick Linearization of Multilinear Programs}},
- journal = {INFORMS J Computing},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-098}
- }
, - "Topology Optimization of Electric Motors using Mesh Projection", International Conference on the Computation of Electromagnetic Fields (COMPUMAG), June 2025.BibTeX TR2025-089 PDF
- @inproceedings{Das2025jun2,
- author = {Das, Ghanendra and Wang, Bingnan and Lin, Chungwei},
- title = {{Topology Optimization of Electric Motors using Mesh Projection}},
- booktitle = {International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-089}
- }
, - "Physics-informed Machine Learning with Heuristic Feedback Control Layer for Autonomous Vehicle Control", IEEE Intelligent Vehicles Symposium (IV), June 2025.BibTeX TR2025-087 PDF
- @inproceedings{Li2025jun2,
- author = {Li, Xianning and Wang, Yebin and Ozbay, Kaan and Jiang, Zhong-Ping},
- title = {{Physics-informed Machine Learning with Heuristic Feedback Control Layer for Autonomous Vehicle Control}},
- booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-087}
- }
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- "Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering", American Control Conference (ACC), July 2025.
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Videos
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Software & Data Downloads
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Optimal Recursive McCormick Linearization of MultiLinear Programs -
Convex sets in Python -
Meta-Learning State Space Models -
Python-based Robotic Control & Optimization Package -
Template Embeddings for Adiabatic Quantum Computation -
Quasi-Newton Trust Region Policy Optimization -
Convergent Inverse Scattering using Optimization and Regularization
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