MERL - Mitsubishi Electric Research Laboratories

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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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  • Awards

    •  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 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


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  • Internships

    • MD1300: Compiler Optimizations for Linear Algebra Kernels

      MERL is looking for a highly motivated individual to work on automatic, compiler based techniques for optimizing linear algebra kernels. The ideal candidate is a Ph.D. student in computer science with extensive experience in compiler design and source code optimization techniques. In particular, the successful candidate will have a strong working knowledge of polyhedral optimization techniques, the LLVM compiler, and Polly. Strong C/C++ skills and knowledge of LLVM at the source level are required. Publication of results in conference proceedings and journals is expected. The expected duration of the internship is 3 months and the start date is flexible.

    • MD1406: Numerical Analysis of Electric Machines

      MERL is seeking a motivated and qualified intern to conduct research in the design, modeling and optimization of electrical machines. The ideal candidate should have solid backgrounds in electromagnetic theory, electric machine design, and numerical modeling techniques (including model reduction), research experiences in electric, magnetic, and thermal modeling and analysis of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and optimization techniques is a strong plus. Senior Ph.D. students in electrical or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

    • 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.


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  • Recent Publications

    •  Di Cairano, S., Danielson, C., "Indirect Adaptive Model Predictive Control and its Application to Uncertain Linear Systems", International Journal of Robust and Nonlinear Control, July 2020.
      BibTeX TR2020-115 PDF
      • @article{DiCairano2020jul,
      • author = {Di Cairano, Stefano and Danielson, Claus},
      • title = {Indirect Adaptive Model Predictive Control and its Application to Uncertain Linear Systems},
      • journal = {International Journal of Robust and Nonlinear Control},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-115}
      • }
    •  Liu, D., Chen, S., Boufounos, P.T., "Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2020.
      BibTeX TR2020-114 PDF
      • @inproceedings{Liu2020jul,
      • author = {Liu, Dehong and Chen, Siheng and Boufounos, Petros T.},
      • title = {Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar},
      • booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-114}
      • }
    •  Xu, X., Dhifallah, O., Mansour, H., Boufounos, P.T., Orlik, P.V., "Robust 3D Tomographic Imaging of the Iononspheric Electron Density", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2020.
      BibTeX TR2020-113 PDF
      • @inproceedings{Xu2020jul,
      • author = {Xu, Xiaojian and Dhifallah, Oussama and Mansour, Hassan and Boufounos, Petros T. and Orlik, Philip V.},
      • title = {Robust 3D Tomographic Imaging of the Iononspheric Electron Density},
      • booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-113}
      • }
    •  Chakrabarty, A., Jha, D., Buzzard, G.T., Wang, Y., Vamvoudakis, K., "Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation", IEEE Transactions on Neural Networks and Learning Systems, July 2020.
      BibTeX TR2020-108 PDF
      • @article{Chakrabarty2020jul2,
      • author = {Chakrabarty, Ankush and Jha, Devesh and Buzzard, Gregery T. and Wang, Yebin and Vamvoudakis, Kyriakos},
      • title = {Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation},
      • journal = {IEEE Transactions on Neural Networks and Learning Systems},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-108}
      • }
    •  Romero, O., Benosman, M., "Finite-Time Convergence in Continuous-Time Optimization", International Conference on Machine Learning (ICML), July 2020.
      BibTeX TR2020-100 PDF
      • @inproceedings{Romero2020jul,
      • author = {Romero, Orlando and Benosman, Mouhacine},
      • title = {Finite-Time Convergence in Continuous-Time Optimization},
      • booktitle = {International Conference on Machine Learning},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-100}
      • }
    •  Quirynen, R., Feng, X., Di Cairano, S., "Inexact Adjoint-based SQP Algorithm for Real-Time Stochastic Nonlinear MPC", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-103 PDF
      • @inproceedings{Quirynen2020jul2,
      • author = {Quirynen, Rien and Feng, Xuhui and Di Cairano, Stefano},
      • title = {Inexact Adjoint-based SQP Algorithm for Real-Time Stochastic Nonlinear MPC},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-103}
      • }
    •  Quirynen, R., Frey, J., Di Cairano, S., "Active-Set based Inexact Interior Point QP Solver for Model Predictive Control", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-105 PDF
      • @inproceedings{Quirynen2020jul3,
      • author = {Quirynen, Rien and Frey, Jonathan and Di Cairano, Stefano},
      • title = {Active-Set based Inexact Interior Point QP Solver for Model Predictive Control},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-105}
      • }
    •  Skvortcov, P., Phillips, I., Forysiak, W., Koike-Akino, T., Kojima, K., Parsons, K., Millar, D.S., "Nonlinearity Tolerant LUT-based Probabilistic Shaping for Extended-Reach Single-Span Links", IEEE Photonics Technology Letters, DOI: 10.1109/LPT.2020.3006737, Vol. 32, No. 16, pp. 967-970, July 2020.
      BibTeX TR2020-107 PDF
      • @article{Skvortcov2020jul,
      • author = {Skvortcov, Pavel and Phillips, Ian and Forysiak, Wladek and Koike-Akino, Toshiaki and Kojima, Keisuke and Parsons, Kieran and Millar, David S.},
      • title = {Nonlinearity Tolerant LUT-based Probabilistic Shaping for Extended-Reach Single-Span Links},
      • journal = {IEEE Photonics Technology Letters},
      • year = 2020,
      • volume = 32,
      • number = 16,
      • pages = {967--970},
      • month = jul,
      • doi = {10.1109/LPT.2020.3006737},
      • issn = {1941-0174},
      • url = {https://www.merl.com/publications/TR2020-107}
      • }
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