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.

  • Researchers

  • Awards

    •  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 Processing
      Brief
      • 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.
    •  
    •  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, Optimization
      Brief
      • 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

    •  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 Security
      Brief
      • 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.
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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 Security
      Brief
      • 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.
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  • Research Highlights

  • Internships

    • MD1761: Blind signal decomposition

      MERL is seeking a self-motivated intern to work on blind signal decomposition. The ideal candidate would be a senior PhD student with solid background in signal processing, sparse representation, and optimization. Prior experience in array signal processing, compressive sensing, and spectrum analysis is preferred. Skills in Python and/or Matlab are required. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The expected duration of the internship is 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

    • SP1747: Learning for Inverse Problems

      The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

    • CA1742: Mixed-Integer Programming for Motion Planning and Control

      MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, motion planning and control of hybrid systems. The research will involve the study and development of numerical optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: branch-and-bound type methods, heuristics for mixed-integer programming (pre-solve, cutting planes, warm starting, integer-feasible solutions), modeling and formulation of MIPs for hybrid control systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on mixed-integer programming or numerical optimization, are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.


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  • Openings


    See All Openings at MERL
  • Recent Publications

    •  Zhu, X., Yuan, L., Kim, K.J., Li, Q., Zhang, J., "Reconfigurable Intelligent Surface-Assisted Spatial Scattering Modulation", IEEE Communications Letters, January 2022.
      BibTeX TR2022-008 PDF
      • @article{Zhu2022jan,
      • author = {Zhu, Xusheng and Yuan, Lei and Kim, Kyeong Jin and Li, Qingqing and Zhang, Jiliang},
      • title = {Reconfigurable Intelligent Surface-Assisted Spatial Scattering Modulation},
      • journal = {IEEE Communications Letters},
      • year = 2022,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2022-008}
      • }
    •  Fujihashi, T., Koike-Akino, T., Watanabe, T., "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}
      • }
    •  Yu, L., Liu, D., Mansour, H., Boufounos, P.T., "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}
      • }
    •  Nohra, C.J., Raghunathan, A., Sahinidis, N.V., "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}
      • }
    •  Quirynen, R., Di Cairano, S., "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}
      • }
    •  Raghunathan, A., "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}
      • }
    •  Raghunathan, A., "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}
      • }
    •  Zhan, S., Wichern, G., Laughman, C.R., Chakrabarty, A., "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}
      • }
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