Abstract. We present a line search multigrid method based on Nash’s MG/OPT multilevel optimization approach for solving discretized versions of convex infinite dimensional optimization problems. Global convergence is proved under fairly minimal requirements on the minimization method used at all grid levels. In particular, our convergence proof does not require that these minimization, or so-ca...

In this paper, we proposed a new bundle adjustment algorithm based on the distance minimization of feature matches, to eliminate the accumulated errors over a sequence for seamless mosaic. The new feature based image mosaic approach improved the stability of the global registration among images. Our experiments show that the new mosaic method is robust. Key–Words: Feature based image mosaic, Bu...

A convexification method is proposed for solving a class of global optimization problems with certain monotone properties. It is shown that this class of problems can be transformed into equivalent concave minimization problems using the proposed convexification schemes. An outer approximation method can then be used to find the global solution of the transformed problem. Applications to mixed-...

By using the canonical dual transformation developed recently, we derive a pair of canonical dual problems for 0-1 quadratic programming problems in both minimization and maximization form. Regardless convexity, when the canonical duals are solvable, no duality gap exists between the primal and corresponding dual problems. Both global and local optimality conditions are given. An algorithm is p...

This paper presents a novel approach to carrying out numerical minimization procedures on the Lie Group SO(3) and related manifolds. The approach constructs a sequence of local parameterizations of the manifold SO(3) rather than relying on a single global parameterization such as Euler angles. Thus, the problems caused by the singularities in these global parameterizations are avoided. This res...

In this paper, we propose a new method to upsample a depth map for 3D video acquisition. The proposed method is based on Markov random fields. The low-resolution depth values are used for data term and the high-resolution color data are exploited for smoothness term. Especially, we apply semi-global energy minimization to reduce computational time. Experimental results show that the proposed me...

In this paper we derive necessary and sufficient conditions for some problems of global minimization. Our approach is based on methods of abstract convexity: we use a representation of an upper semicontinuous function as the lower envelope of a family of convex functions. We discuss applications of conditions obtained to the examination of some tractable sufficient conditions for the global min...

We develop a minimization functional in order to regularize the inverse problem associated with three-dimensional ionospheric stochastic tomography. This functional is designed to yield, upon minimization, a solution which maximizes the frequency content of the solution below a certain cutoo, while keeping 2 constant. We show how this functional can be rewritten in terms of the correlation func...

The dissertation by Maryam Fazel, “Matrix Rank Minimization with applications”, [3], focuses on minimizing ranks over matrices of convex sets which is genrally an NP-hard problem. The work provides a new heuristics for solving Rank Minimization Problem (RMP) for PSD matrices, which contrary to hitherto developed heuristics, do not require an initial point, are numerically efficient and provide ...

With System-on-Chip design, IP blocks form routing obstacles that deteriorate global interconnect delay. In this paper, we present a new approach for obstacle-avoiding rectilinear minimal delay Steiner tree (OARMDST) construction. We formalize the solving of minimum delay tree through the concept of an extended minimization function, and trade the objective into a top-down recursion, which wise...