We present an active-set method for minimizing an objective that is the sum of a convex quadratic and `1 regularization term. Unlike two-phase methods that combine a first-order active set identification step and a subspace phase consisting of a cycle of conjugate gradient iterations, the method presented here has the flexibility of computing one of three possible steps at each iteration: a rel...

‎The object of this paper is to present a new iteration process‎. ‎We will show that our process is faster than the known recent iterative schemes‎. ‎We discuss stability results of our iteration and prove some results in the context of uniformly convex Banach space for Suzuki generalized nonexpansive mappings‎. ‎We also present a numerical example for proving the rate of convergence of our res...

Abstract Two classes of recursive algorithms for computing the extreme solutions of the matrix equations X+AT X−1A = Q and X−AT X−1A = Q are presented. The Per Step Algorithms are based on the fixed point iteration method and its variations; the proposed Per Step Algorithm is an inversion free variant of the fixed point iteration method. The Doubling Algorithms are based on the cyclic reduction...

In this paper, we address the challenges faced by Value Iteration Networks (VIN) in handling larger input maps and mitigating impact of accumulated errors caused increased iterations. We propose a novel approach, with Gated Summarization Module (GS-VIN), which incorporates two main improvements: 1) employing an Adaptive Strategy module to reduce number iterations; 2) introducing summarize itera...

Abstract Intelligent manufacturing is a continuously evolving and iteratively improving system engineering that requires long-term adherence step-by-step implementation. The continuous process changes during scenario iteration have stimulated the demand for simulation tools in intelligent management processes. However, currently, existing mostly simulate coherence rarely verify stability of pro...

In this position paper we examine the limitation of region growing segmentation techniques to extract semantically meaningful objects from an image. We propose a region growing algorithm that performs on a semantic level, driven by the knowledge of what each region represents at every iteration step of the merging process. This approach utilizes simultaneous segmentation and labeling of regions...

Our approach for global loop transformations aimed at optimizing data transfer and storage is based on an extended polytope model. The transformations are performed in two steps: a placement step that maps the individual polytopes to a common iteration space and an ordering step defining an order in the common iteration space. Recently, Danckaert has proposed to split the placement step itself ...

For the orthogonal direction algorithm, the iteration direction is the direction vector, and the iteration error of the adaptive filter is caused by the input vector. These two directions are not the same one, which leads to more deviation for the iteration error. In this paper, an orthogonal direction algorithm using direction error is presented to settle this problem, which the iteration erro...

In this paper, we propose a three-step iteration scheme for nonself asymptotically nonexpansive type mappings in a complete CAT(0) metric space and establish necessary and sufficient conditions for convergence of this process to a fixed point of nonself asymptotically nonexpansive type mappings. We also establish a strong convergence result. These results generalize and unify many important res...

In this position paper we examine the limitation of region growing segmentation techniques to extract semantically meaningful objects from an image. We propose a region growing algorithm that performs on a semantic level, driven by the knowledge of what each region represents at every iteration step of the merging process. This approach utilizes simultaneous segmentation and labeling of regions...