There exists a surface of a convex polyhedron P and a partition L of P into geodesic convex polygons such that there are no connected"edge"unfoldings of P without self-intersections (whose spanning tree is a subset of the edge skeleton of L).

We initiate a systematic study of tolerant testers of image properties or, equivalently, algorithms that approximate the distance from a given image to the desired property (that is, the smallest fraction of pixels that need to change in the image to ensure that the image satisfies the desired property). Image processing is a particularly compelling area of applications for sublinear-time algor...

It is shown that a subset of a uniformly convex normed space is nearly convex if and only if its closure is convex. Also, a normed space satisfying a mild completeness property is strictly convex if and only if every metrically convex subset is convex. 1 Classical and constructive mathematics The arguments in this paper conform to constructive mathematics in the sense of Errett Bishop. This mea...

Gordon G Johnson* ([email protected]), Department of Mathematics, University of Houston, Houston, TX 77204-3008. The Closure in a Hilbert Space of a PreHilbert Space CHEBYSHEV Set Fails to be a CHEBYSHEV Set. Preliminary report. E is the real inner product space that is union of all finite-dimensional Euclidean spaces, S is a certain bounded nonconvex set in the E having the property that every...

Maximum a posteriori (MAP) inference is one of the fundamental inference tasks in graphical models. MAP inference is in general NP-hard, making approximate methods of interest for many problems. One successful class of approximate inference algorithms is based on linear programming (LP) relaxations. The augmented Lagrangian method can be used to overcome a lack of strict convexity in LP relaxat...

Consider a class of convex minimization problems for which the objective function is the sum of a smooth convex function and a non-smooth convex regularity term. This class of problems includes several popular applications such as compressive sensing and sparse group LASSO. In this thesis, we introduce a general class of approximate proximal splitting (APS) methods for solving such minimization...

In this paper, we propose a variable metric method for unconstrained multiobjective optimization problems (MOPs). First, sequence of points is generated using different positive definite matrices in the generic framework. It proved that accumulation are Pareto critical points. Then, without convexity assumption, strong convergence established proposed method. Moreover, use common matrix to appr...

Given a nonconvex function f(x) that is an average of n smooth functions, we design stochastic first-order methods to find its approximate stationary points. The performance of our new methods depend on the smallest (negative) eigenvalue −σ of the Hessian. This parameter σ captures how strongly nonconvex f(x) is, and is analogous to the strong convexity parameter for convex optimization. At lea...

The purpose of this paper is to examine a nonlinear spectral semidefinite programming method to solve problems with bilinear matrix inequality (BMI) constraints. Such optimization programs arise frequently in automatic control and are difficult to solve due to the inherent non-convexity. The method we discuss here is of augmented Lagrangian type and uses a succession of unconstrained subproblem...