Convex Optimization

Alternating direction algorithms for total variation deconvolution in image reconstruction

  • Tao Min
  • Junfeng Yang
    • Categories Convex Optimization, Nonsmooth Optimization, Unconstrained Optimization Tags , , , ,

    Image restoration and reconstruction from blurry and noisy observation is known to be ill-posed. To stabilize the recovery, total variation (TV) regularization was introduced by Rudin, Osher and Fatemi in \cite{LIR92}, which has demonstrated superiority in preserving image edges. However, the nondifferentiability of TV makes the underlying optimization problems difficult to solve. In this paper, … Read more

    Local superlinear convergence of polynomial-time interior-point methods for hyperbolic cone optimization problems

  • Yurii Nesterov
  • Levent Tunçel
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming Tags , , , , ,

    In this paper, we establish the local superlinear convergence property of some polynomial-time interior-point methods for an important family of conic optimization problems. The main structural property used in our analysis is the logarithmic homogeneity of self-concordant barrier function, which must have {\em negative curvature}. We propose a new path-following predictor-corrector scheme, which work only … Read more

    A Facial Reduction Algorithm for Finding Sparse SOS Representations

  • Masakazu Muramatsu
  • Hayato Waki
    • Categories Convex Optimization, Global Optimization Theory, Semi-definite Programming Tags , , ,

    Facial reduction algorithm reduces the size of the positive semidefinite cone in SDP. The elimination method for a sparse SOS polynomial ([3]) removes unnecessary monomials for an SOS representation. In this paper, we establish a relationship between a facial reduction algorithm and the elimination method for a sparse SOS polynomial. Citation Technical Report CS-09-02, Department … Read more

    PARNES: A rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals

  • Ming Gu
  • Lek-Heng Lim
  • Cinna Julie Wu
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Optimization Software Benchmark Tags , , , , , , ,

    In this article we propose an algorithm, NESTA-LASSO, for the LASSO problem (i.e., an underdetermined linear least-squares problem with a one-norm constraint on the solution) that exhibits linear convergence under the restricted isometry property (RIP) and some other reasonable assumptions. Inspired by the state-of-the-art sparse recovery method, NESTA, we rely on an accelerated proximal gradient … Read more

    Sparse and Low-Rank Matrix Decomposition Via Alternating Direction Methods

  • Junfeng Yang
  • Xiao-Ming Yuan
    • Categories Convex Optimization Tags , , , , ,

    The problem of recovering the sparse and low-rank components of a matrix captures a broad spectrum of applications. Authors in [4] proposed the concept of “rank-sparsity incoherence” to characterize the fundamental identifiability of the recovery, and derived practical sufficient conditions to ensure the high possibility of recovery. This exact recovery is achieved via solving a … Read more

    On closedness conditions, strong separation, and convex dualit y

  • Miklós Ujvári
    • Categories Convex Optimization

    In the paper, we describe various applications of the closedness and duality theorems of [7] and [8]. First, the strong separability of a polyhedron and a linear image of a convex set is characterized. Then,it is shown how stability conditions (known from the generalized Fenchel-Rockafellar duality theory) can be reformulated as closedness conditions. Finally, we … Read more

    A Simpler Approach to Matrix Completion

  • Benjamin Recht
    • Categories Convex Optimization, Data-Mining, Statistics Tags , , , , , ,

    This paper provides the best bounds to date on the number of randomly sampled entries required to reconstruct an unknown low rank matrix. These results improve on prior work by Candes and Recht, Candes and Tao, and Keshavan, Montanari, and Oh. The reconstruction is accomplished by minimizing the nuclear norm, or sum of the singular … Read more

    A Unifying Polyhedral Approximation Framework for Convex Optimization

  • Dimitri Bertsekas
  • Huizhen Yu
    • Categories Convex Optimization, Network Optimization Tags , , ,

    We propose a unifying framework for polyhedral approximation in convex optimization. It subsumes classical methods, such as cutting plane and simplicial decomposition, but also includes new methods, and new versions/extensions of old methods, such as a simplicial decomposition method for nondifferentiable optimization, and a new piecewise linear approximation method for convex single commodity network flow … Read more

    Solving log-determinant optimization problems by a Newton-CG primal proximal point algorithm

  • Defeng Sun
  • Kim-Chuan Toh
  • Chengjing Wang
    • Categories Convex Optimization, Semi-definite Programming, Statistics Tags , ,

    We propose a Newton-CG primal proximal point algorithm for solving large scale log-determinant optimization problems. Our algorithm employs the essential ideas of the proximal point algorithm, the Newton method and the preconditioned conjugate gradient solver. When applying the Newton method to solve the inner sub-problem, we find that the log-determinant term plays the role of … Read more

    The Legendre-Fenchel Conjugate of the Product of Two positive definite Quadratic Forms

  • Yun-Bin Zhao
    • Categories Convex Optimization Tags , , ,

    It is well-known that the Legendre-Fenchel conjugate of a positive definite quadratic form can be explicitly expressed as another positive definite quadratic form, and that the conjugate of the sum of several positive definite quadratic forms can be expressed via inf-convolution. However, the Legendre-Fenchel conjugate of the product of two positive definite quadratic forms is … Read more