We consider the problem of minimizing ∫ ` 0 √ ξ2 +K2(s) ds for a planar curve having fixed initial and final positions and directions. The total length ` is free. Here s is the variable of arclength parametrization, K(s) is the curvature of the curve and ξ > 0 a parameter. This problem comes from a model of geometry of vision due to Petitot, Citti and Sarti. We study existence of local and glob...

For Rn constrained global optimization problem, a new auxiliary function with one parameter is proposed for escaping the current local minimizer. First, a new definition of filled function is given and under mild assumptions, it is proved to be a filled function. Then a new algorithm is designed according to the theoretical analysis and some preliminary numerical results are given to demonstrat...

We examine the local convergence properties of pattern search methods, complementing the previously established global convergence properties for this class of algorithms. We show that the step-length control parameter which appears in the definition of pattern search algorithms provides a reliable asymptotic measure of first-order stationarity. This gives an analytical justification for a trad...

Abstract. This paper presents a set of complete solutions to a class of polynomial optimization problems. By using the so-called sequential canonical dual transformation developed in the author’s recent book [Gao, D.Y. (2000), Duality Principles in Nonconvex Systems: Theory, Method and Applications, Kluwer Academic Publishers, Dordrecht/Boston/London, xviii + 454 pp], the nonconvex polynomials ...

Knowing the fact that the main weakness of the most standard methods including k-means and hierarchical data clustering is their sensitivity to initialization and trapping to local minima, this paper proposes a modification of convex data clustering  in which there is no need to  be peculiar about how to select initial values. Due to properly converting the task of optimization to an equivalent...

While convergence of the Alternating DirectionMethod of Multipliers (ADMM) on convex problems is well studied, convergence on nonconvex problems is only partially understood. In this paper, we consider the Gaussian phase retrieval problem, formulated as a linear constrained optimization problem with a biconvex objective. The particular structure allows for a novel application of the ADMM. It ca...

The Hard Thresholding Pursuit (HTP) is a class of truncated gradient descent methods for finding sparse solutions of l0-constrained loss minimization problems. The HTP-style methods have been shown to have strong approximation guarantee and impressive numerical performance in high dimensional statistical learning applications. However, the current theoretical treatment of these methods has trad...

The active contour model [8,9,2] is one of the most well-known variational methods in image segmentation. In a recent paper by Bresson et al. [1], a link between the active contour model and the variational denoising model of Rudin-Osher-Fatemi (ROF) [10] was demonstrated. This relation provides a method to determine the global minimizer of the active contour model. In this paper, we propose a ...

In this paper we consider the problem of minimizing a (possibly nonconvex) quadratic function with a quadratic constraint. We point out some new properties of the problem. In particular, in the rst part of the paper, we show that (i) given a KKT point that is not a global minimizer, it is easy to nd a \better" feasible point; (ii) strict complementarity holds at the local-nonglobal minimizer. I...

A family of integral functionals F which, in a simplified way, model material microstructure occupying a two-dimensional domain Ω and which take account of surface energy and a variable well-depth is studied. It is shown that there is a critical well-depth, whose scaling with the surface energy density and domain dimensions is given, below which the state u = 0 is the global minimizer of a typi...