On local quasi efficient solutions for nonsmooth vector optimization

We are interested in local quasi efficient solutions for nonsmooth vector optimization problems under new generalized approximate invexity assumptions. We formulate necessary and sufficient optimality conditions based on Stampacchia and Minty types of vector variational inequalities involving Clarke's generalized Jacobians. We also establish the relationship between local quasi weak efficient solutions and vector critical points

Numerical computation of preimage domain and condenser capacity for a strip with rectilinear slits

Let $S$ be a strip in ${\mathbb{C}}$ and let $E\subset S$ be a union of disjoint segments. For the domain $S \setminus E$, we construct a numerical conformal mapping onto a domain bordered by smooth Jordan curves. To this aim, we use the boundary integral equation method from [19]. In particular, we apply this method to study the conformal capacity of the condenser $(S, E).$ Numerical experiments on several model problems show that our method is able to reach a very high level of precision when estimating the condenser capacity

An improved DSS for a local human resource development emphasizing basic TQM practice

Nowadays, it is very crucial to increase competency skills of unemployed fresh graduates in Malaysia so as to overcome the rising of unemployment rate.Hence, training schemes and development courses are fruitful solutions to fill up the gap between what students studied at universities and what exactly labor markets need. Therefore, in this paper we propose an improved Decision Support System (DSS) for a Local Human Resource Development (LHRD), which emphasized the Total Quality Management practice.Specifically, the output is the DSS for the incorporative LHRD model, which is further improved by the Web-based training process to enhance the overall delivery of various training and development schemes. The implementation of the model is able to increase knowledge, skills and capacities of fresh graduates, thus increase their productivity as employees along with job satisfaction

Numerical computation of Mityuk's function and radius for circular-radial slit domains

We consider Mityuk's function and radius which have been proposed in \cite{Mit} as generalizations of the reduced modulus and conformal radius to the cases of multiply connected domains. We present a numerical method to compute Mityuk's function and radius for canonical domains that consist of the unit disk with circular/radial slits. Our method is based on the boundary integral equation with the generalized Neumann kernel. Special attention is given to the validation of the theoretical results on the existence of critical points and the boundary behavior of Mityuk's radius

The Motion of a Point Vortex in Multiply Connected Polygonal Domains

We study the motion of a single point vortex in simply and multiply connected polygonal domains. In case of multiply connected domains, the polygonal obstacles can be viewed as the cross-sections of 3D polygonal cylinders. First, we utilize conformal mappings to transfer the polygonal domains onto circular domains. Then, we employ the Schottky-Klein prime function to compute the Hamiltonian governing the point vortex motion in circular domains. We compare between the topological structures of the contour lines of the Hamiltonian in symmetric and asymmetric domains. Special attention is paid to the interaction of point vortex trajectories with the polygonal obstacles. In this context, we discuss the effect of symmetry breaking, and obstacle location and shape on the behavior of vortex motion

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flowcomputation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we develop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow computation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation

Multilevel optimization for dense motion estimation

This research has been oriented towards the design of a new technique for fast and reliable dense motion estimation. We used variational models of optical flow computation to estimate the dense motion in a sequence of images.We have been interested in developing a multilevel optimization solver to produce accurate optical flow estimation for real-time applications.To the best of our knowledge, two-ways multilevel optimization techniques are used for the first time in the context of a computer vision problem. We evaluated the performance of different optimization techniques developed in the context of optical flow computation with different variational models.In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we developed the use of efficient multilevel schemes for computing the optical flow.More precisely, we evaluated the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/Opt), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/Opt).The FMG/Opt algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on three image sequences using four models of optical flow with different computational efforts show that the FMG/Opt algorithm outperforms significantly both the TN and MR/Opt algorithms in terms of the computational work and the quality of the optical flow estimation

An Investigation of Smooth TV-Like Regularization in the Context of the Optical Flow Problem

Total variation (TV) is widely used in many image processing problems including the regularization of optical flow estimation. In order to deal with non differentiability of the TV regularization term, smooth approximations have been considered in the literature. In this paper, we investigate the use of three known smooth TV approximations, namely: the Charbonnier, Huber and Green functions. We establish the maximum theoretical error of these approximations and discuss their performance evaluation when applied to the optical flow problem