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Eric C. Chi

  1. Techniques for Solving Sudoku Puzzles.

    Чт, 04/05/2012 - 15:01 — SomNambul
    Authors: Kenneth Lange, Eric C. Chi
    Subjects: Optimization and Control
    Abstract

    Solving Sudoku puzzles is one of the most popular pastimes in the world.
    Puzzles range in difficulty from easy to very challenging; the hardest puzzles
    tend to have the most empty cells. The current paper explains and compares
    three algorithms for solving Sudoku puzzles. Backtracking, simulated annealing,
    and alternating projections are generic methods for attacking combinatorial
    optimization problems. Our results favor backtracking. It infallibly solves a
    Sudoku puzzle or deduces that a unique solution does not exist.

  2. Robust Parametric Classification and Variable Selection by a Minimum Distance Criterion.

    Чт, 09/29/2011 - 15:01 — SomNambul
    Authors: Eric C. Chi, David W. Scott
    Subjects: Methodology
    Abstract

    We investigate a robust penalized logistic regression algorithm based on a
    minimum distance criterion. Influential outliers are often associated with the
    explosion of parameter vector estimates, but in the context of standard
    logistic regression, the bias due to outliers always causes the parameter
    vector to implode, that is shrink towards the zero vector. Thus, using
    LASSO-like penalties to perform variable selection in the presence of outliers
    can result in missed detections of relevant covariates.

  3. Making Tensor Factorizations Robust to Non-Gaussian Noise.

    Пнд, 10/18/2010 - 15:01 — SomNambul
    Authors: Tamara G. Kolda, Eric C. Chi
    Subjects: Numerical Analysis
    Abstract

    Tensors are multi-way arrays, and the Candecomp/Parafac (CP) tensor
    factorization has found application in many different domains. The CP model is
    typically fit using a least squares objective function, which is a maximum
    likelihood estimate under the assumption of i.i.d. Gaussian noise. We
    demonstrate that this loss function can actually be highly sensitive to
    non-Gaussian noise. Therefore, we propose a loss function based on the 1-norm
    because it can accommodate both Gaussian and grossly non-Gaussian
    perturbations.

Math-arch

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