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 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, Optimization
  Brief
  - 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.
- 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.
See All Awards for Optimization
News & Events
- 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, Robotics
  Brief
  - 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.
- 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, Optimization
  Abstract
  - Next-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
Research Highlights
- Edge-Assisted Internet of Vehicles for Smart Mobility
Internships
- CA0153: Internship - High-Fidelity Visualization and Simulation for Space Applications
  
  MERL is seeking a highly motivated graduate student to develop high-fidelity full-stack GNC simulators for space applications. The ideal candidate has strong experience with rendering engines, synthetic image generation, and computer vision, as well as familiarity with spacecraft dynamics, motion planning, and state estimation. The developed software should allow for closed-loop execution with the synthetic imagery, and ideally allow for real-time visualization. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.
  Required Specific Experience
  - Current enrollment in a graduate program in Aerospace, Computer Science, Robotics, Mechanical, Electrical Engineering, or a related field
  - Experience with one or more of Blender, Unreal, Unity, along with their APIs
  - Strong programming skills in one or more of Matlab, Python, and/or C/C++
- MS0098: Internship - Control and Estimation for Large-Scale Thermofluid Systems
  
  MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.
- EA0149: Internship - Electric Motor Design Optimization
  
  MERL is seeking a motivated and qualified individual to conduct research on physics informed neural network-based modeling for electric motor design optimization. Ideal candidates should be Ph.D. students with solid background and proven publication record in one or more of the following research areas: 2D/3D electromagnetic modeling and simulation, analytical modeling methods for electromagnetics and iron losses (e.g. magnetic equivalent circuit), and machine learning-based surrogate modeling. Strong coding skill with ANSYS or open-source FEM software and Python-based learning library is a must and prior experience with running jobs over cluster is a plus. Start date for this internship is flexible and the duration is 3-6 months.
  Required Specific Experience
  - Experience with modeling and simulations for motor design
See All Internships for Optimization
Openings
See All Openings at MERL
Recent Publications
- Zhang, Q., Avraamidou, S., Paulson, J.A., Thakkar, V., Wang, Z., Chiang, L., Braun, B., Rathi, T., Chakrabarty, A., Sorouifar, F., Tang, W.-T., Guertin, F., Munoz, P., Sampat, A., "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}
  - }
- Cardona, G., Vasile, C.-I., Di Cairano, S., "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}
  - }
- Chavez Armijos, A., Berntorp, K., Di Cairano, S., "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}
  - }
- Pavlasek, N., Di Cairano, S., Weiss, A., "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}
  - }
- Shimane, Y., Di Cairano, S., Ho, K., Weiss, A., "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}
  - }
- Raghunathan, A., Cardonha, C., Bergman, D., Nohra, C.J., "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}
  - }
- Das, G., Wang, B., Lin, C., "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}
  - }
- Li, X., Wang, Y., Ozbay, K., Jiang, Z.-P., "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}
  - }
See All Publications for Optimization
Videos

[MERL Seminar Series Summer 2025] Robust Trajectory Planning and Control

FDPP: Fine-tune Diffusion Policy with Human Preference

Motion Planning for Information Acquisition via Continuous-time Successive Convexification

[MERL Seminar Series Spring 2025] Optimizing the Path Towards Plastic-Free Oceans

Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Robot Teams

[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

Five Axis Additive Manufacturing

Fast Pattern Search in Big Data

Globally Optimal Power Flow
Software & Data Downloads