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
Daniel N.
Nikovski
Stefano
Di Cairano
Arvind
Raghunathan
Toshiaki
Koike-Akino
Philip V.
Orlik
Rien
Quirynen
Mouhacine
Benosman
Petros T.
Boufounos
Kieran
Parsons
Matthew E.
Brand
Ye
Wang
Karl
Berntorp
Ankush
Chakrabarty
Hassan
Mansour
Christopher R.
Laughman
Devesh K.
Jha
Pu
(Perry)
Wang
Yebin
Wang
Scott A.
Bortoff
Hongbo
Sun
Jianlin
Guo
Saleh
Nabi
Diego
Romeres
Kyeong Jin
(K.J.)
Kim
Dehong
Liu
Yanting
Ma
Jinyun
Zhang
Hongtao
Qiao
Avishai
Weiss
William S.
Yerazunis
Marcus
Greiff
Chungwei
Lin
Rui
Ma
Marcel
Menner
Gordon
Wichern
Jose
Amaya
Abraham M.
Goldsmith
Norihiro
Nishiuma
Jay
Thornton
Bingnan
Wang
Jing
Zhang
Joshua
Rapp
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Awards
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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 ProcessingBrief- 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.
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AWARD Best conference paper of IEEE PES-GM 2020 Date: June 18, 2020
Awarded to: Tong Huang, Hongbo Sun, K.J. Kim, Daniel Nikovski, Le Xie
MERL Contacts: Kyeong Jin (K.J.) Kim; Daniel N. Nikovski; Hongbo Sun
Research Areas: Data Analytics, Electric Systems, OptimizationBrief- A paper on A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Natural Disasters, written by Tong Huang, a former MERL intern from Texas A&M University, has been selected as one of the Best Conference Papers at the 2020 Power and Energy Society General Meeting (PES-GM). IEEE PES-GM is the flagship conference for the IEEE Power and Energy Society. The work was done in collaboration with Hongbo Sun, K. J. Kim, and Daniel Nikovski from MERL, and Tong's advisor, Prof. Le Xie from Texas A&M University.
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News & Events
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EVENT Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
Speaker: Prof. Melanie Zeilinger, ETH
Location: Virtual Event
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information SecurityBrief- MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
Prof. Melanie Zeilinger from ETH .
Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).
Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).
Registration: https://mailchi.mp/merl/merlvoh2021
Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction
Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
- MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
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EVENT Prof. Ashok Veeraraghavan of Rice University to give keynote at MERL's Virtual Open House Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
Speaker: Prof. Ashok Veeraraghavan, Rice University
Location: Virtual Event
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information SecurityBrief- MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
Prof. Ashok Veeraraghavan from Rice University.
Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).
Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Veeraraghavan's talk is scheduled for 1:15pm - 1:45pm (EST).
Registration: https://mailchi.mp/merl/merlvoh2021
Keynote Title: Computational Imaging: Beyond the limits imposed by lenses.
Abstract: The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of the incident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging.
First, we discuss our lab’s recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.
- MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
See All News & Events for Optimization -
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Research Highlights
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Internships
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SP1710: Distributed Learning and Computing over Networks
MERL is seeking a highly motivated and qualified intern to join the Signal Processing Group for a three month internship program. The ideal candidate will be expected to carry out research on collaborative learning between infrastructure, devices, and vehicles. The candidate is expected to develop distributed learning for various applications, including autonomous driving, smart infrastructure, mobile networks, etc.. The candidate should have knowledge of federated learning and distributed computing, networking and over-the-air-computation. Knowledge of scheduling, spectrum management, and mathematical analysis for convergence testing is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.
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MS1717: Estimation and Optimization for Large-Scale Systems
MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems. 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.
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MD1697: Integrated design of mechatronic systems
MERL is seeking a highly motivated and qualified individual to conduct research in model-based mechatronic system design. The ideal candidate should have solid backgrounds in motor and drives, multi-body dynamics, design optimization, and coding skills. Demonstrated experience on hand-on mechatronic system integration, and simulation/optimization software such as Matlab is a necessity. Ph.D. students in mechanical engineering, robotics, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.
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Openings
See All Openings at MERL -
Recent Publications
- , "Overhead Reduction for Graph-Based Point Cloud Delivery Using Non-Uniform Quantization", IEEE International Conference on Consumer Electronics (ICCE), January 2022.BibTeX TR2022-005 PDF
- @inproceedings{Fujihashi2022jan,
- author = {Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi},
- title = {Overhead Reduction for Graph-Based Point Cloud Delivery Using Non-Uniform Quantization},
- booktitle = {IEEE International Conference on Consumer Electronics (ICCE)},
- year = 2022,
- month = jan,
- url = {https://www.merl.com/publications/TR2022-005}
- }
- , "Fast and High-Quality Blind Multi-Spectral Image Pansharpening", IEEE Transactions on Geoscience and Remote Sensing, January 2022.BibTeX TR2022-004 PDF
- @article{Yu2022jan,
- author = {Yu, Lantao and Liu, Dehong and Mansour, Hassan and Boufounos, Petros T.},
- title = {Fast and High-Quality Blind Multi-Spectral Image Pansharpening},
- journal = {IEEE Transactions on Geoscience and Remote Sensing},
- year = 2022,
- month = jan,
- url = {https://www.merl.com/publications/TR2022-004}
- }
- , "SDP-quality bounds via convex quadratic relaxations for global optimization of mixed-integer quadratic programs", Mathematical Programming B, December 2021.BibTeX TR2022-001 PDF
- @article{Nohra2021dec,
- author = {Nohra, Carlos J. and Raghunathan, Arvind and Sahinidis, Nikolaos V.},
- title = {SDP-quality bounds via convex quadratic relaxations for global optimization of mixed-integer quadratic programs},
- journal = {Mathematical Programming B},
- year = 2021,
- month = dec,
- url = {https://www.merl.com/publications/TR2022-001}
- }
- , "Sequential Quadratic Programming Algorithm for Real-Time Mixed-Integer Nonlinear MPC", IEEE Conference on Decision and Control (CDC), December 2021.BibTeX TR2021-147 PDF
- @inproceedings{Quirynen2021dec,
- author = {Quirynen, Rien and Di Cairano, Stefano},
- title = {Sequential Quadratic Programming Algorithm for Real-Time Mixed-Integer Nonlinear MPC},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2021,
- month = dec,
- url = {https://www.merl.com/publications/TR2021-147}
- }
- , "Homogeneous Formulation of Convex Quadratic Programs for Infeasibility Detection", IEEE Conference on Decision and Control (CDC), December 2021.BibTeX TR2021-150 PDF
- @inproceedings{Raghunathan2021dec2,
- author = {Raghunathan, Arvind},
- title = {Homogeneous Formulation of Convex Quadratic Programs for Infeasibility Detection},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2021,
- month = dec,
- url = {https://www.merl.com/publications/TR2021-150}
- }
- , "Homogeneous Formulation of Convex Quadratic Programs for Infeasibility Detection", IEEE Conference on Decision and Control (CDC), December 2021.BibTeX TR2021-150 PDF
- @inproceedings{Raghunathan2021dec,
- author = {Raghunathan, Arvind},
- title = {Homogeneous Formulation of Convex Quadratic Programs for Infeasibility Detection},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2021,
- month = dec,
- url = {https://www.merl.com/publications/TR2021-150}
- }
- , "Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes", Advances in Neural Information Processing Systems (NeurIPS), December 2021.BibTeX TR2021-149 PDF
- @inproceedings{Zhan2021dec,
- author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2021,
- month = dec,
- url = {https://www.merl.com/publications/TR2021-149}
- }
- , "Machine Learning for design and optimization of photonic devices" in Machine Learning for Future Fiber Optic Communication Systems (Elsevier Book), November 2021.BibTeX TR2021-142 PDF
- @incollection{Kojima2021nov,
- author = {Kojima, Keisuke and Tang, Yingheng and Wang, Ye and Koike-Akino, Toshiaki},
- title = {Machine Learning for design and optimization of photonic devices},
- booktitle = {Machine Learning for Future Fiber Optic Communication Systems (Elsevier Book)},
- year = 2021,
- month = nov,
- url = {https://www.merl.com/publications/TR2021-142}
- }
- , "Overhead Reduction for Graph-Based Point Cloud Delivery Using Non-Uniform Quantization", IEEE International Conference on Consumer Electronics (ICCE), January 2022.
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Videos
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[MERL Seminar Series 2021] Integration of Analytics Techniques for Algorithmic Sports Betting
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Multiview Sensing with Unknown Permutations: An Optimal Transport Approach
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Imaging for inverse scattering in Reflection Tomography
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Electric Satellite Station Keeping, Attitude Control, and Momentum Management by MPC
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Polar Coding with Chemical Reaction Networks for Molecular Communications
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EMI reduction in PWM inverters using adaptive frequency modulated carriers
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Globally Optimal Power Flow
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Five Axis Additive Manufacturing
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Fast Pattern Search in Big Data
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Software Downloads
