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Showing 1 to 9 of 9 for “"Accelerated Algorithms"”.

  1. Learning-accelerated algorithms for simulation and optimization

    … simulation and optimization. Specifically, two algorithms are developed: (1) a variance reduction algorithm for Monte Carlo simulations of mean-field particle systems, and (2) a global optimization algorithm for noisy expensive functions. For the variance reduction algorithm, we develop an …

    uiuc Repository record for Learning-accelerated algorithms for simulation and optimization (opens in a new tab)

  2. Accelerated algorithms for constrained optimization and control

    … control literature with an effort to lead to accelerated convergence for the case when no constraints are present. In this thesis, we propose a new high-order tuner based algorithm that can accommodate the presence of equality and inequality constraints. We leverage the linear dependence in …

    mit Repository record for Accelerated algorithms for constrained optimization and control (opens in a new tab)

  3. GPU-accelerated algorithms for the resource-constrained assignment problem

    The student, Olivia Reynen, accepted the attached license on 2020-05-01 at 19:39.

    uiuc Repository record for GPU-accelerated algorithms for the resource-constrained assignment problem (opens in a new tab)

  4. The design and development of GPU accelerated algorithms for ab initio integrals and integral derivatives illustrated on ab initio quantum and hybrid QM/MM dynamics

    … twoelectron integrals. To date, a number of GPU accelerated two-electron integral implementations have been developed significantly improving the performance of a static quantum mechanical (QM) calculation. However, when performing an ab-initio QM gradient calculation, optimization, QM or Hybrid …

    cape-town Repository record for The design and development of GPU accelerated algorithms for ab initio integrals and integral derivatives illustrated on ab initio quantum and hybrid QM/MM dynamics (opens in a new tab)

  5. Faster k-means clustering.

    … in vector data automatically. We present three accelerated algorithms that compute exactly the same clusters much faster than the standard method. First, we redesign Hamerly's algorithm to use k heaps to avoid checking distance bounds for all n points, with little empirical gain. Second, we use …

    baylor Repository record for Faster k-means clustering. (opens in a new tab)

  6. A boundary element method with surface conductive absorbers for 3-D analysis of nanophotonics

    … Moreover, we implement two different FFT-accelerated algorithms for the periodic non-absorbing region and the non-periodic absorbing region. In addition, we use perturbation theory and Poynting's theorem, respectively, to calculate the field decay rate due to the surface conductivity. We …

    mit Repository record for A boundary element method with surface conductive absorbers for 3-D analysis of nanophotonics (opens in a new tab)

  7. Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications

    … be realized without efficient optimization algorithms that can handle extremely large-scale and dense data from real applications. Although the convex relaxations of these problems can be reformulated as either linear programming, second-order cone programming or semidefinite programming …

    columbia-diss Repository record for Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications (opens in a new tab)

  8. Greed, hedging, and acceleration in convex optimization

    … message of the thesis is that, surprisingly, algorithms which are individually suboptimal can be combined to achieve accelerated convergence rates. This phenomenon can be intuively understood as "hedging" between safe strategies (e.g. slowly converging algorithms) and aggressive strategies …

    mit Repository record for Greed, hedging, and acceleration in convex optimization (opens in a new tab)

  9. Efficient algorithms for distributed learning, optimization and belief systems over networks

    … put into the design of efficient distributed algorithms that take into account the communication constraints and make coordinated decisions in a fully distributed manner. In this dissertation, we focus on the principled design and analysis of distributed algorithms for optimization, learning …

    uiuc Repository record for Efficient algorithms for distributed learning, optimization and belief systems over networks (opens in a new tab)