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Creators/Authors contains: "London, Palma"

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  1. ; ; ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, Operations Research)
    This paper presents a black-box framework for accelerating packing optimization solvers. Our method applies to packing linear programming problems and a family of convex programming problems with linear constraints. The framework is designed for high-dimensional problems, for which the number of variables n is much larger than the number of measurements m. Given an [Formula: see text] problem, we construct a smaller [Formula: see text] problem, whose solution we use to find an approximation to the optimal solution. Our framework can accelerate both exact and approximate solvers. If the solver being accelerated produces an α-approximation, then we produce a [Formula: see text]-approximation of the optimal solution to the original problem. We present worst-case guarantees on run time and empirically demonstrate speedups of two orders of magnitude. 
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    Full Text Available 
  3. ; ; ; (, 34th Conference on Neural Information Processing Systems (NeurIPS))
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ; (, 34th Conference on Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  5. ; ; ; (, IEEE/ACM Transactions on Networking)
    Full Text Available 
  6. ; ; ; (, ACM SIGMETRICS Performance Evaluation Review)
    Full Text Available