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

  • News & Events

    •  TALK   Universal Differential Equations for Scientific Machine Learning
      Date & Time: Thursday, May 7, 2020; 12:00 PM
      Speaker: Christopher Rackauckas, MIT
      MERL Host: Christopher Laughman
      Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
      Brief
      • In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reco nciling data that is at odds with simplified models without requiring "big data". In this talk we discuss a new methodology, universal differential equations (UDEs), which augment scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating climate simulations by 15,000x, can be handled by training UDEs.
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    •  NEWS   Stefano Di Cairano Appointed IPC Vice-Chair for the 7th IFAC Symposium on NMPC (2021)
      Date: July 7, 2021 - July 14, 2021
      Where: Bratislava, Slovakia
      MERL Contact: Stefano Di Cairano
      Research Areas: Control, Machine Learning, Optimization
      Brief
      • MERL researcher Stefano Di Cairano has been appointed as Vice-Chair for Industry of the International Program Committee of the 7th IFAC Symposium on Nonlinear Model Predictive Control, which will be held in Bratislava, Slovakia, in July 2021.
        IFAC NMPC is the main symposium focused on model predictive control, theory, methods and applications, includes contributions on control, optimization, and machine learning research, and is held every 3 years.
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  • Internships

    • CA1399: Optimization Algorithms for Stochastic Predictive Control

      MERL is looking for a highly motivated individual to work on tailored numerical optimization algorithms and applications of stochastic learning-based model predictive control (MPC) methods. The research will involve the study and development of novel optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), convex and non-convex optimization, machine learning, numerical optimization and (inverse) optimal control. PhD students in engineering or mathematics with a focus on stochastic (learning-based) MPC 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 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.

    • CA1401: Formal Synthesis for Planning and Control for Autonomous Systems

      The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on planning and control by formal methods, in particular temporal logics specifications and their synthesis by mixed-integer inequalities. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Computer Science or related program, with focus on Control Theory. The ideal candidate will have experience in (one or more of) formal methods, particularly temporal logics and signal temporal logics, reachability analysis, abstractions of dynamical systems, hybrid predictive control, and mixed integer programming. Good programming skills in Matlab (or alternatively Python) are required, working knowledge of C/C++ is a plus. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

    • MD1405: Mechanism design for mobility

      Mobility; externalities; mechanism design. We are looking for a talented and driven individual to help us design an efficient and equitable mobility solution. This position requires a deep understanding of mechanism design and at least some programming ability. Preference will be given to candidates with a background in transportation.


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  • Recent Publications

    •  Fujihashi, T., Koike-Akino, T., Watanabe, T., Orlik, P.V., "Overhead Reduction in Graph-Based Point Cloud Delivery", IEEE International Conference on Communications (ICC), May 2020.
      BibTeX TR2020-061 PDF Video
      • @inproceedings{Fujihashi2020may2,
      • author = {Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi and Orlik, Philip V.},
      • title = {Overhead Reduction in Graph-Based Point Cloud Delivery},
      • booktitle = {IEEE International Conference on Communications (ICC)},
      • year = 2020,
      • month = may,
      • url = {https://www.merl.com/publications/TR2020-061}
      • }
    •  Fehenberger, T., Millar, D.S., Koike-Akino, T., Kojima, K., Parsons, K., Griesser, H., "Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables", IEEE Journal of Lightwave Technology, DOI: 10.1109/JLT.2020.2987210, Vol. 38, No. 10, pp. 2825 - 2833, April 2020.
      BibTeX TR2020-051 PDF
      • @article{Fehenberger2020apr2,
      • author = {Fehenberger, Tobias and Millar, David S. and Koike-Akino, Toshiaki and Kojima, Keisuke and Parsons, Kieran and Griesser, Helmut},
      • title = {Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables},
      • journal = {IEEE Journal of Lightwave Technology},
      • year = 2020,
      • volume = 38,
      • number = 10,
      • pages = {2825 -- 2833},
      • month = apr,
      • doi = {10.1109/JLT.2020.2987210},
      • issn = {1558-2213},
      • url = {https://www.merl.com/publications/TR2020-051}
      • }
    •  Koike-Akino, T., Wang, P., Pajovic, M., Sun, H., Orlik, P.V., "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: A Deep Learning Approach", IEEE Access, DOI: 10.1109/ACCESS.2020.2991129, April 2020.
      BibTeX TR2020-054 PDF Data
      • @article{Koike-Akino2020apr,
      • author = {Koike-Akino, Toshiaki and Wang, Pu and Pajovic, Milutin and Sun, Haijian and Orlik, Philip V.},
      • title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: A Deep Learning Approach},
      • journal = {IEEE Access},
      • year = 2020,
      • month = apr,
      • doi = {10.1109/ACCESS.2020.2991129},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2020-054}
      • }
    •  Fehenberger, T., Millar, D.S., Koike-Akino, T., Kojima, K., Parsons, K., "Parallel-Amplitude Architecture and Subset Ranking for Fast Distribution Matching", IEEE Transactions on Communications, DOI: 10.1109/TCOMM.2020.2966693, Vol. 68, No. 4, pp. 1981-1990, April 2020.
      BibTeX TR2020-050 PDF
      • @article{Fehenberger2020apr,
      • author = {Fehenberger, Tobias and Millar, David S. and Koike-Akino, Toshiaki and Kojima, Keisuke and Parsons, Kieran},
      • title = {Parallel-Amplitude Architecture and Subset Ranking for Fast Distribution Matching},
      • journal = {IEEE Transactions on Communications},
      • year = 2020,
      • volume = 68,
      • number = 4,
      • pages = {1981--1990},
      • month = apr,
      • doi = {10.1109/TCOMM.2020.2966693},
      • issn = {1558-0857},
      • url = {https://www.merl.com/publications/TR2020-050}
      • }
    •  Ma, Y., Lodhi, M.A., Mansour, H., Boufounos, P.T., Liu, D., "Inverse Multiple Scattering With Phaseless Measurements", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9053430, April 2020, pp. 1519-1523.
      BibTeX TR2020-041 PDF Video
      • @inproceedings{Ma2020apr,
      • author = {Ma, Yanting and Lodhi, Muhammad Asad and Mansour, Hassan and Boufounos, Petros T. and Liu, Dehong},
      • title = {Inverse Multiple Scattering With Phaseless Measurements},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2020,
      • pages = {1519--1523},
      • month = apr,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP40776.2020.9053430},
      • issn = {2379-190X},
      • isbn = {978-1-5090-6631-5},
      • url = {https://www.merl.com/publications/TR2020-041}
      • }
    •  Xia, Y., Wang, P., Berntorp, K., Koike-Akino, T., Mansour, H., Pajovic, M., Boufounos, P.T., Orlik, P.V., "Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9054614, April 2020, pp. 4900-4904.
      BibTeX TR2020-044 PDF Video
      • @inproceedings{Xia2020apr,
      • author = {Xia, Yuxuan and Wang, Pu and Berntorp, Karl and Koike-Akino, Toshiaki and Mansour, Hassan and Pajovic, Milutin and Boufounos, Petros T. and Orlik, Philip V.},
      • title = {Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2020,
      • pages = {4900--4904},
      • month = apr,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP40776.2020.9054614},
      • issn = {2379-190X},
      • isbn = {978-1-5090-6631-5},
      • url = {https://www.merl.com/publications/TR2020-044}
      • }
    •  Xie, Y., Liu, D., Mansour, H., Boufounos, P.T., "Robust Parameter Estimation of Contaminated Damped Exponentials", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9053507, April 2020, pp. 5500-5504.
      BibTeX TR2020-052 PDF Video
      • @inproceedings{Xie2020apr,
      • author = {Xie, Youye and Liu, Dehong and Mansour, Hassan and Boufounos, Petros T.},
      • title = {Robust Parameter Estimation of Contaminated Damped Exponentials},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2020,
      • pages = {5500--5504},
      • month = apr,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP40776.2020.9053507},
      • issn = {2379-190X},
      • isbn = {978-1-5090-6631-5},
      • url = {https://www.merl.com/publications/TR2020-052}
      • }
    •  Burghal, D., Kim, K.J., Guo, J., Orlik, P.V., Hori, T., Sumi, T., Nagai, Y., "Multi-Channel Delay Sensitive Scheduling for Convergecast Network", IEEE Wireless Communications and Networking Conference (WCNC), April 2020.
      BibTeX TR2020-036 PDF
      • @inproceedings{Burghal2020apr,
      • author = {Burghal, Daoud and Kim, Kyeong Jin and Guo, Jianlin and Orlik, Philip V. and Hori, Toshinori and Sumi, Takenori and Nagai, Yukimasa},
      • title = {Multi-Channel Delay Sensitive Scheduling for Convergecast Network},
      • booktitle = {IEEE Wireless Communications and Networking Conference (WCNC)},
      • year = 2020,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2020-036}
      • }
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  • Videos

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