Perfect dimensional ratios and optimality of some empirical numerical standards

Experience and observations often underlie some widely used numerical characteristics. The problem is in the extent to which such characteristics are optimal. The paper presents results of theoretical analysis of the most frequently used numerical characteristics regarding the number of classes in classification systems, of the base of the number system, and of the level … Read more

Nonsmooth Methods for Control Design with Integral Quadratic Constraints

We develop an optimization technique to compute local solutions to synthesis problems subject to integral quadratic constraints (IQCs). We use the fact that IQCs may be transformed into semi-infinite maximum eigenvalue constraints over the frequency axis and approach them via nonsmooth optimization methods. We develop a suitable spectral bundle method and prove its convergence in … Read more

On the application of the spectral projected gradient method in image segmentation

We investigate the application of the nonmonotone spectral projected gradient (SPG) method to a region-based variational model for image segmentation. We consider a “discretize-then-optimize” approach and solve the resulting nonlinear optimization problem by an alternating minimization procedure that exploits the SPG2 algorithm by Birgin, Martì­nez and Raydan (SIAM J. Optim., 10(4), 2000). We provide a … Read more

The Cyclic Block Conditional Gradient Method for Convex Optimization Problems

In this paper we study the convex problem of optimizing the sum of a smooth function and a compactly supported non-smooth term with a specific separable form. We analyze the block version of the generalized conditional gradient method when the blocks are chosen in a cyclic order. A global sublinear rate of convergence is established … Read more

Exact solutions to Super Resolution on semi-algebraic domains in higher dimensions

We investigate the multi-dimensional Super Resolution problem on closed semi-algebraic domains for various sampling schemes such as Fourier or moments. We present a new semidefinite programming (SDP) formulation of the l1-minimization in the space of Radon measures in the multi-dimensional frame on semi-algebraic sets. While standard approaches have focused on SDP relaxations of the dual … Read more

A new step size rule in Yan et al.’s self-adaptive projection method

In this paper, we propose a new step size rule to accelerate Yan et al.’s self-adaptive projection method. Under the new step size strategy, the superiority of modified projection method is verified through theory to numerical experiments. Citation College of Communications Engineering, PLA University of Science and Technology, Nanjing, 210007, China 01/29/2015 Article Download View … Read more

An optimization-based method for feature ranking in nonlinear regression problems

In this work we consider the feature ranking problem where, given a set of training instances, the task is to associate a score to the features in order to assess their relevance. Feature ranking is a very important tool for decision support systems, and may be used as an auxiliary step of feature selection to … Read more

A Parallel Evolution Strategy for an Earth Imaging Problem in Geophysics

In this paper we propose a new way to compute a warm starting point for a challenging global optimization problem related to Earth imaging in geophysics. The warm start consists of a velocity model that approximately solves a full-waveform inverse problem at low frequency. Our motivation arises from the availability of massively parallel computing platforms … Read more

Computationally Efficient Approach for the Minimization of Volume Constrained Vector-Valued Ginzburg-Landau Energy Functional

The minimization of volume constrained vector-valued Ginzburg-Landau energy functional is considered in the present study. It has many applications in computational science and engineering, like the conservative phase separation in multiphase systems (such as the spinodal decomposition), phase coarsening in multiphase systems, color image segmentation and optimal space partitioning. A computationally efficient algorithm is presented … Read more

Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss

We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We assume that each machine in the distributed computing system has access to a local empirical loss function, constructed with i.i.d. data sampled from a common distribution. We propose a communication-efficient distributed algorithm to … Read more