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
Abraham P.
Vinod
Ye
Wang
Kieran
Parsons
Avishai
Weiss
Scott A.
Bortoff
Diego
Romeres
Matthew
Brand
Petros T.
Boufounos
Hassan
Mansour
Pu
(Perry)
Wang
Jianlin
Guo
Vedang M.
Deshpande
Hongbo
Sun
Dehong
Liu
Hongtao
Qiao
Bingnan
Wang
Purnanand
Elango
Gordon
Wichern
Chungwei
Lin
Yanting
Ma
Saviz
Mowlavi
William S.
Yerazunis
Nobuyuki
Yoshikawa
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 Contact: Arvind Raghunathan
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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TALK [MERL Seminar Series 2026] Zac Manchester presents talk titled Is locomotion really that hard… and other musings on the virtues of simplicity Date & Time: Tuesday, January 20, 2026; 12:00 PM
Speaker: Zac Manchester, MIT
MERL Host: Pedro Miraldo
Research Areas: Computer Vision, Control, Optimization, RoboticsAbstract
For decades, legged locomotion was a challenging research topic in robotics. In the last few years, however, both model-based and reinforcement-learning approaches have not only demonstrated impressive performance in laboratory settings, but are now regularly deployed "in the wild." One surprising feature of these successful controllers is how simple they can be. Meanwhile, Art Bryson’s timeless advice to control engineers, “Be wise – linearize,” seems to be increasingly falling out of fashion and at risk of being forgotten by the next generation of practitioners. This talk will discuss several recent works from my group that try to push the limits of how simple locomotion (and, possibly, manipulation) controllers for general-purpose robots can be from several different viewpoints, while also making connections to state-of-the-art generative AI methods like diffusion policies.
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NEWS MERL researchers present 3 papers at AIAA SciTech Forum 2026 Date: January 12, 2026 - January 16, 2026
Where: Orlando, Florida
MERL Contacts: Stefano Di Cairano; Purnanand Elango; Kento Tomita; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Dynamical Systems, OptimizationBrief- MERL researchers presented 3 papers at the recently concluded AIAA SciTech Forum 2026 in Orlando, Florida. The AIAA SciTech Forum is the flagship conference (more than 6,000 from 48 countries) of the American Institute of Aeronautics and Astronautics, the world's largest professional technical society dedicated to aerospace.
The papers presented by MERL researchers covered 1) a powered descent decision making approach to maximize the probability of safe landing, 2) a set-based robust, optimal, and resilient control architecture for autonomous precision landing, and 3) a continuous-time safe control policy for passively-safe spacecraft rendezvous on a Near Rectilinear Halo Orbit using successive convexification.
- MERL researchers presented 3 papers at the recently concluded AIAA SciTech Forum 2026 in Orlando, Florida. The AIAA SciTech Forum is the flagship conference (more than 6,000 from 48 countries) of the American Institute of Aeronautics and Astronautics, the world's largest professional technical society dedicated to aerospace.
See All News & Events for Optimization -
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Research Highlights
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Internships
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SA0156: Internship - Stochastic Model Predictive Control with Generative Models for Smart Building Control
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CI0197: Internship - Embodied AI & Humanoid Robotics
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SA0282: Internship - AI augmented optimization
See All Internships for Optimization -
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Openings
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MS0268: Research Scientist - Multiphysical Systems
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CI0177: Postdoctoral Research Fellow - Agentic AI
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OR0052: Research Scientist - Optimization Algorithms
See All Openings at MERL -
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Recent Publications
- , "A Comparative Study of MINLP and MPVC Formulations for Solving Complex Nonlinear Decision-Making Problems in Aerospace Applications", Optimal Control Applications and Methods, February 2026.BibTeX TR2026-024 PDF
- @article{Ghezzi2026feb,
- author = {Ghezzi, Andrea and Nurkanović, Armin and Weiss, Avishai and Diehl, Moritz and {Di Cairano}, Stefano},
- title = {{A Comparative Study of MINLP and MPVC Formulations for Solving Complex Nonlinear Decision-Making Problems in Aerospace Applications}},
- journal = {Optimal Control Applications and Methods},
- year = 2026,
- month = feb,
- url = {https://www.merl.com/publications/TR2026-024}
- }
- , "Continuous-Time Successive Convexification for Passively-Safe Spacecraft Rendezvous on a Near Rectilinear Halo Orbit", AIAA SciTech Forum 2026, January 2026.BibTeX TR2026-016 PDF
- @inproceedings{Elango2026jan,
- author = {Elango, Purnanand and Vinod, Abraham P. and Kitamura, Kenji and Acikmese, Behcet and {Di Cairano}, Stefano and Weiss, Avishai},
- title = {{Continuous-Time Successive Convexification for Passively-Safe Spacecraft Rendezvous on a Near Rectilinear Halo Orbit}},
- booktitle = {AIAA SciTech Forum 2026},
- year = 2026,
- month = jan,
- url = {https://www.merl.com/publications/TR2026-016}
- }
- , "Robust Optimal Control for Autonomous Precision Landing via Set-based Dynamic Programming", AIAA SciTech Forum, January 2026.BibTeX TR2026-015 PDF
- @inproceedings{Kamath2026jan,
- author = {Kamath, Abhinav and Vinod, Abraham P. and Elango, Purnanand and {Di Cairano}, Stefano and Weiss, Avishai},
- title = {{Robust Optimal Control for Autonomous Precision Landing via Set-based Dynamic Programming}},
- booktitle = {AIAA SciTech Forum},
- year = 2026,
- month = jan,
- url = {https://www.merl.com/publications/TR2026-015}
- }
- , "Relaxed barrier function based model predictive control with hard input constraints", IEEE Control Systems Letters, DOI: 10.1109/LCSYS.2025.3645287, Vol. 9, pp. 2927-2932, 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,
- volume = 9,
- pages = {2927--2932},
- month = dec,
- doi = {10.1109/LCSYS.2025.3645287},
- issn = {2475-1456},
- 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 Control Systems Letters},
- 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), DOI: 10.1109/CDC57313.2025.11312593, December 2025, pp. 1769-1776.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,
- pages = {1769--1776},
- month = dec,
- doi = {10.1109/CDC57313.2025.11312593},
- 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}
- }
- , "A Comparative Study of MINLP and MPVC Formulations for Solving Complex Nonlinear Decision-Making Problems in Aerospace Applications", Optimal Control Applications and Methods, February 2026.
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Videos
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Software & Data Downloads
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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 -
Optimal Recursive McCormick Linearization of MultiLinear Programs
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