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
Arvind
Raghunathan
Toshiaki
Koike-Akino
Daniel N.
Nikovski
Christopher R.
Laughman
Philip V.
Orlik
Yebin
Wang
Ye
Wang
Kieran
Parsons
Abraham P.
Vinod
Scott A.
Bortoff
Diego
Romeres
Matthew
Brand
Avishai
Weiss
Petros T.
Boufounos
Hassan
Mansour
Pu
(Perry)
Wang
Jianlin
Guo
Vedang M.
Deshpande
Hongbo
Sun
Dehong
Liu
Hongtao
Qiao
Bingnan
Wang
Gordon
Wichern
Chungwei
Lin
Yanting
Ma
Saviz
Mowlavi
Yuki
Shirai
Purnanand
Elango
William S.
Yerazunis
Jinyun
Zhang
Abraham
Goldsmith
Shingo
Kobori
Pedro
Miraldo
Alexander
Schperberg
Anoop
Cherian
Radu
Corcodel
Jordan
Leung
Joshua
Rapp
Kenji
Inomata
Jing
Liu
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Awards
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AWARD Mitsubishi Electric and MERL work recognized with IEEJ Distinguished Paper Award Date: June 1, 2025
Awarded to: Arvind Raghunathan, Daniel Nikovski
MERL Contacts: Daniel N. Nikovski; Arvind Raghunathan
Research Areas: Electric Systems, OptimizationBrief- A publication jointly authored by Mitsubishi Electric Corporation's Advanced Technology Center (ATC) and MERL researchers has been recognized with the 2025 IEEJ Distinguished Paper Award by the Institute of Electrical Engineers Japan. The paper titled "Power Band Model Based on Flow Network and Weekly Unit Commitment Problem Considering Reserve Market" published in the IEEJ Transactions on Power and Energy presents a novel Unit Commitment formulation for scheduling the generator operations. Arvind Raghunathan and Daniel Nikovksi were co-authors on this publication.
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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: 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.
See All Awards for Optimization -
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News & Events
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NEWS MERL Researchers at NeurIPS 2025 presented 2 conference papers, 5 workshop papers, and organized a workshop. Date: December 2, 2025 - December 7, 2025
Where: San Diego
MERL Contacts: Petros T. Boufounos; Anoop Cherian; Radu Corcodel; Stefano Di Cairano; Chiori Hori; Christopher R. Laughman; Suhas Anand Lohit; Pedro Miraldo; Saviz Mowlavi; Kuan-Chuan Peng; Arvind Raghunathan; Diego Romeres; Yuki Shirai; Abraham P. Vinod; Pu (Perry) Wang
Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & AudioBrief- MERL researchers presented 2 main-conference papers and 5 workshop papers, as well as organized a workshop, at NeurIPS 2025.
Main Conference Papers:
1) Sorachi Kato, Ryoma Yataka, Pu Wang, Pedro Miraldo, Takuya Fujihashi, and Petros Boufounos, "RAPTR: Radar-based 3D Pose Estimation using Transformer", Code available at: https://github.com/merlresearch/radar-pose-transformer
2) Runyu Zhang, Arvind Raghunathan, Jeff Shamma, and Na Li, "Constrained Optimization From a Control Perspective via Feedback Linearization"
Workshop Papers:
1) Yuyou Zhang, Radu Corcodel, Chiori Hori, Anoop Cherian, and Ding Zhao, "SpinBench: Perspective and Rotation as a Lens on Spatial Reasoning in VLMs", NeuriIPS 2025 Workshop on SPACE in Vision, Language, and Embodied AI (SpaVLE) (Best Paper Runner-up)
2) Xiaoyu Xie, Saviz Mowlavi, and Mouhacine Benosman, "Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization", Workshop on Machine Learning and the Physical Sciences (ML4PS)
3) Spencer Hutchinson, Abraham Vinod, François Germain, Stefano Di Cairano, Christopher Laughman, and Ankush Chakrabarty, "Quantile-SMPC for Grid-Interactive Buildings with Multivariate Temporal Fusion Transformers", Workshop on UrbanAI: Harnessing Artificial Intelligence for Smart Cities (UrbanAI)
4) Yuki Shirai, Kei Ota, Devesh Jha, and Diego Romeres, "Sim-to-Real Contact-Rich Pivoting via Optimization-Guided RL with Vision and Touch", Worskhop on Embodied World Models for Decision Making
5) Mark Van der Merwe and Devesh Jha, "In-Context Policy Iteration for Dynamic Manipulation", Workshop on Embodied World Models for Decision Making
Workshop Organized:
MERL members co-organized the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips25/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce AI Research), Kevin Smith (Massachusetts Institute of Technology), and Joshua B. Tenenbaum (Massachusetts Institute of Technology).
- MERL researchers presented 2 main-conference papers and 5 workshop papers, as well as organized a workshop, at NeurIPS 2025.
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NEWS Abraham Vinod Delivers Invited Talks at The University of Texas at Austin and The University of Texas at Dallas Date: November 11, 2025 - November 13, 2025
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Optimization, RoboticsBrief- MERL researcher Abraham Vinod was invited to present MERL's latest research at the University of Texas at Austin and The University of Texas at Dallas this November. His talk discussed a tractable set-based method for a broad class of robust control problems with nonlinear dynamics and bounded uncertainty, with applications to powered descent guidance and drone motion planning problems. Additionally, he also presented MERL's recent research on environmental monitoring using hetereogenous robots, with applications in disaster management and search-and-rescue.
See All News & Events for Optimization -
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Research Highlights
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Internships
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MS0254: Internship - Decentralized Data Assimilation for Large Scale Systems
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CA0279: Internship - Heterogeneous multi-agent planning and control
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EA0235: Internship - Planning and Control of Mobile Manipulators
See All Internships for Optimization -
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Openings
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OR0052: Research Scientist - Optimization Algorithms
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CI0177: Postdoctoral Research Fellow - Agentic AI
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MS0268: Research Scientist - Multiphysical Systems
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CA0093: Research Scientist - Control for Autonomous Systems
See All Openings at MERL -
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Recent Publications
- , "Relaxed barrier function based model predictive control with hard input constraints", IEEE Control Systems Letters, December 2025.BibTeX TR2026-005 PDF
- @article{Castroviejo-Fernandez2025dec,
- author = {Castroviejo-Fernandez, Miguel and Leung, Jordan},
- title = {{Relaxed barrier function based model predictive control with hard input constraints}},
- journal = {IEEE Control Systems Letters},
- year = 2025,
- month = dec,
- url = {https://www.merl.com/publications/TR2026-005}
- }
- , "Motion Planning for Information Acquisition via Continuous-time Successive Convexification", IEEE Conference on Decision and Control (CDC), December 2025.BibTeX TR2025-170 PDF
- @inproceedings{Uzun2025dec,
- author = {Uzun, Samet and Acikmese, Behcet and {Di Cairano}, Stefano},
- title = {{Motion Planning for Information Acquisition via Continuous-time Successive Convexification}},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2025,
- month = dec,
- url = {https://www.merl.com/publications/TR2025-170}
- }
- , "Set-based lossless convexification for a class of robust nonlinear optimal control problems", IEEE Conference on Decision and Control (CDC), December 2025.BibTeX TR2025-160 PDF
- @inproceedings{Vinod2025dec,
- author = {Vinod, Abraham P. and Kamath, Abhinav and Weiss, Avishai and {Di Cairano}, Stefano},
- title = {{Set-based lossless convexification for a class of robust nonlinear optimal control problems}},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2025,
- month = dec,
- url = {https://www.merl.com/publications/TR2025-160}
- }
- , "Electric Motor Topology Optimization via Rotated Filter Projection and Adjoint Sensitivities", IEEE Transactions on Magnetics, December 2025.BibTeX TR2025-164 PDF
- @article{Das2025dec,
- author = {Das, Ghanendra and Wang, Bingnan and Lin, Chungwei},
- title = {{Electric Motor Topology Optimization via Rotated Filter Projection and Adjoint Sensitivities}},
- journal = {IEEE Transactions on Magnetics},
- year = 2025,
- month = dec,
- url = {https://www.merl.com/publications/TR2025-164}
- }
- , "Constrained Optimization From a Control Perspective via Feedback Linearization", The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NuerIPS), December 2025.BibTeX TR2025-165 PDF
- @inproceedings{Zhang2025dec,
- author = {Zhang, Runyu and Raghunathan, Arvind and Shamma, Jeff and Li, Na},
- title = {{Constrained Optimization From a Control Perspective via Feedback Linearization}},
- booktitle = {The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NuerIPS)},
- year = 2025,
- month = dec,
- url = {https://www.merl.com/publications/TR2025-165}
- }
- , "Optimization-Based Phase-Constrained x-Axis Crossing Control for Station-Keeping on Libration Point Orbits", Journal of the Astronautical Sciences, DOI: 10.1007/s40295-025-00543-1, Vol. 72, No. 6, pp. 59, November 2025.BibTeX TR2026-004 PDF
- @article{Shimane2025dec3,
- author = {Shimane, Yuri and Ho, Koki and Weiss, Avishai},
- title = {{Optimization-Based Phase-Constrained x-Axis Crossing Control for Station-Keeping on Libration Point Orbits}},
- journal = {Journal of the Astronautical Sciences},
- year = 2025,
- volume = 72,
- number = 6,
- pages = 59,
- month = dec,
- doi = {10.1007/s40295-025-00543-1},
- url = {https://www.merl.com/publications/TR2026-004}
- }
- , "Meta-Learning for Physically-Constrained Neural System Identification", Neurocomputing, DOI: 10.1016/j.neucom.2025.130945, Vol. 651, pp. 130945, October 2025.BibTeX TR2025-159 PDF
- @article{Chakrabarty2025nov,
- author = {Chakrabarty, Ankush and Wichern, Gordon and Deshpande, Vedang M. and Vinod, Abraham P. and Berntorp, Karl and Laughman, Christopher R.},
- title = {{Meta-Learning for Physically-Constrained Neural System Identification}},
- journal = {Neurocomputing},
- year = 2025,
- volume = 651,
- pages = 130945,
- month = nov,
- doi = {10.1016/j.neucom.2025.130945},
- issn = {0925-2312},
- url = {https://www.merl.com/publications/TR2025-159}
- }
- , "SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity", IEEE International Conference on Computer Vision (ICCV), October 2025.BibTeX TR2025-146 PDF Presentation
- @inproceedings{Piedade2025oct,
- author = {{{Piedade, Valter and Chitturi, Sidhartha and Gaspar, Jose and Govindu, Venu and Miraldo, Pedro}}},
- title = {{{SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity}}},
- booktitle = {IEEE International Conference on Computer Vision (ICCV)},
- year = 2025,
- month = oct,
- url = {https://www.merl.com/publications/TR2025-146}
- }
- , "Relaxed barrier function based model predictive control with hard input constraints", IEEE Control Systems Letters, December 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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