Search Results - subject:"convex optimization"

Path-following gradient-based decomposition algorithms for separable convex optimization

Dinh, Quoc Tran; Necoara, Ion; Diehl, Moritz - In: Journal of Global Optimization 59 (2014) 1, pp. 59-80

separable convex optimization problems. Unlike path-following Newton methods considered in the literature, this algorithm does …

Persistent link: https://www.econbiz.de/10010845803

Efficient algorithms for robust and stable principal component pursuit problems

Aybat, Necdet; Goldfarb, Donald; Ma, Shiqian - In: Computational Optimization and Applications 58 (2014) 1, pp. 1-29

a convex optimization problem, namely the robust principal component pursuit (RPCP). Moreover, if the observed data …

Persistent link: https://www.econbiz.de/10010847445

Sensitivity analysis for inference with partially identifiable covariance matrices

Max G’Sell; Shen-Orr, Shai; Tibshirani, Robert - In: Computational Statistics 29 (2014) 3, pp. 529-546

In some multivariate problems with missing data, pairs of variables exist that are never observed together. For example, some modern biological tools can produce data of this form. As a result of this structure, the covariance matrix is only partially identifiable, and point estimation requires...

Persistent link: https://www.econbiz.de/10010848017

A solution algorithm for non-convex mixed integer optimization problems with only few continuous variables

Schöbel, Anita; Scholz, Daniel - In: European Journal of Operational Research 232 (2014) 2, pp. 266-275

Geometric branch-and-bound techniques are well-known solution algorithms for non-convex continuous global optimization problems with box constraints. Several approaches can be found in the literature differing mainly in the bounds used.

Persistent link: https://www.econbiz.de/10011052715

Numerical study of learning algorithms on Stiefel manifold

Kanamori, Takafumi; Takeda, Akiko - In: Computational Management Science 11 (2014) 4, pp. 319-340

Convex optimization methods are used for many machine learning models such as support vector machine. However, the … learning methods not requiring convexity have emerged. In this paper, we study non-convex optimization problems on the Stiefel … examples of non-convex optimization problems in machine learning and apply three nonlinear optimization methods for finding a …

Persistent link: https://www.econbiz.de/10010949666

Calibrating probability distributions with convex-concave-convex functions: application to CDO pricing

Veremyev, Alexander; Tsyurmasto, Peter; Uryasev, Stan; … - In: Computational Management Science 11 (2014) 4, pp. 341-364

This paper considers a class of functions referred to as convex-concave-convex (CCC) functions to calibrate unimodal or multimodal probability distributions. In discrete case, this class of functions can be expressed by a system of linear constraints and incorporated into an optimization...

Persistent link: https://www.econbiz.de/10010949668

A Modified Non-Monotone BFGS Method for Non-Convex Unconstrained Optimization

Liu, Liying; Yao, Shengwei; Wei, Zengxin - In: Asia-Pacific Journal of Operational Research (APJOR) 31 (2014) 05, pp. 1450033-1

In this paper, a modified non-monotone BFGS (MNBFGS) method for non-convex unconstrained optimization is proposed. Under some mild conditions, the global convergence of the given method is established, when the objective function is non-convex. Preliminary numerical comparisons, which show the...

Persistent link: https://www.econbiz.de/10010940524

A VIF-based optimization model to alleviate collinearity problems in multiple linear regression

Jou, Yow-Jen; Huang, Chien-Chia; Cho, Hsun-Jung - In: Computational Statistics 29 (2014) 6, pp. 1515-1541

In this paper, we address data collinearity problems in multiple linear regression from an optimization perspective. We propose a novel linearly constrained quadratic programming model, based on the concept of the variance inflation factor (VIF). We employ the perturbation method that involves...

Persistent link: https://www.econbiz.de/10011151871

Centers of sets with symmetry or cyclicity properties

Seeger, Alberto; Torki, Mounir - In: TOP: An Official Journal of the Spanish Society of … 22 (2014) 2, pp. 716-738

with convex optimization theory. In fact, computing the so-called “incenter” of a solid closed convex cone is a matter of … solving a nonsmooth convex optimization program. On the other hand, the concept of the incenter of a solid closed convex cone … has a bearing on the complexity analysis and design of algorithms for convex optimization programs under conic constraints …

Persistent link: https://www.econbiz.de/10010995357

An infeasible-point subgradient method using adaptive approximate projections

Lorenz, Dirk; Pfetsch, Marc; Tillmann, Andreas - In: Computational Optimization and Applications 57 (2014) 2, pp. 271-306

We propose a new subgradient method for the minimization of nonsmooth convex functions over a convex set. To speed up computations we use adaptive approximate projections only requiring to move within a certain distance of the exact projections (which decreases in the course of the algorithm)....

Persistent link: https://www.econbiz.de/10010998277