Zhiwei (Tony) Qin

HIPAD – A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

  • Ioannis G. Akrotirianakis
  • Zhiwei (Tony) Qin
  • Amit Chakraborty
  • Xiaocheng Tang
    • Categories Convex and Nonsmooth Optimization, Data-Mining, Quadratic Programming Tags , , , ,

    We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipliers in the first phase, followed by an interior-point method … Read more

    Joint Variable Selection for Data Envelopment Analysis via Group Sparsity

  • Zhiwei (Tony) Qin
  • Irene Song
    • Categories Applications - OR and Management Sciences, Convex Optimization Tags , , ,

    This study develops a data-driven group variable selection method for data envelopment analysis (DEA), a non-parametric linear programming approach to the estimation of production frontiers. The proposed method extends the group Lasso (least absolute shrinkage and selection operator) designed for variable selection on (often predefined) groups of variables in linear regression models to DEA models. … Read more

    An Alternating Direction Method for Total Variation Denoising

  • Donald Goldfarb
  • Zhiwei (Tony) Qin
    • Categories Convex and Nonsmooth Optimization Tags , , , ,

    We consider the image denoising problem using total variation (TV) regularization. This problem can be computationally challenging to solve due to the non-differentiability and non-linearity of the regularization term. We propose an alternating direction augmented Lagrangian (ADAL) method, based on a new variable splitting approach that results in subproblems that can be solved efficiently and … Read more

    Structured Sparsity via Alternating Direction Methods

  • Donald Goldfarb
  • Zhiwei (Tony) Qin
    • Categories Convex Optimization, Nonsmooth Optimization Tags , , , ,

    We consider a class of sparse learning problems in high dimensional feature space regularized by a structured sparsity-inducing norm which incorporates prior knowledge of the group structure of the features. Such problems often pose a considerable challenge to optimization algorithms due to the non-smoothness and non-separability of the regularization term. In this paper, we focus … Read more

    Efficient Block-coordinate Descent Algorithms for the Group Lasso

  • Donald Goldfarb
  • Zhiwei (Tony) Qin
  • Katya Scheinberg
    • Categories Convex and Nonsmooth Optimization Tags , , , , ,

    We present two algorithms to solve the Group Lasso problem [Yuan & Lin]. First, we propose a general version of the Block Coordinate Descent (BCD) algorithm for the Group Lasso that employs an efficient approach for optimizing each subproblem. We show that it exhibits excellent performance when the groups are of moderate sizes. For large … Read more