Browsing by Subject "Hybrid methods"

Now showing 1 - 5 of 5

Open Access
Catching up with method and process practice: an industry-informed baseline for researchers
(Institute of Electrical and Electronics Engineers Inc., 2019) Klünder, J.; Hebig, R.; Tell, P.; Kuhrmann, M.; Nakatumba-Nabende, J.; Heldal, R.; Krusche, S.; Fazal-Baqaie, M.; Felderer, M.; Bocco, M. F. G.; Küpper, S.; Licorish, S. A.; Lopez, G.; McCaffery, F.; Top, Ö. Ö.; Prause, C. R.; Prikladnicki, R.; Tüzün, Eray; Pfahl, D.; Schneider, K.; MacDonell, S. G.
Software development methods are usually not applied by the book. Companies are under pressure to continuously deploy software products that meet market needs and stakeholders' requests. To implement efficient and effective development processes, companies utilize multiple frameworks, methods and practices, and combine these into hybrid methods. A common combination contains a rich management framework to organize and steer projects complemented with a number of smaller practices providing the development teams with tools to complete their tasks. In this paper, based on 732 data points collected through an international survey, we study the software development process use in practice. Our results show that 76.8% of the companies implement hybrid methods. Company size as well as the strategy in devising and evolving hybrid methods affect the suitability of the chosen process to reach company or project goals. Our findings show that companies that combine planned improvement programs with process evolution can increase their process' suitability by up to 5%.
Open Access
Enhancing block cimmino for sparse linear systems with dense columns via schur complement
(Society for Industrial and Applied Mathematics, 2023-04-07) Torun, F. S.; Manguoglu, M.; Aykanat, Cevdet
The block Cimmino is a parallel hybrid row-block projection iterative method successfully used for solving general sparse linear systems. However, the convergence of the method degrades when angles between subspaces spanned by the row-blocks are far from being orthogonal. The density of columns as well as the numerical values of their nonzeros are more likely to contribute to the nonorthogonality between row-blocks. We propose a novel scheme to handle such “dense” columns. The proposed scheme forms a reduced system by separating these columns and the respective rows from the original coefficient matrix and handling them via the Schur complement. Then the angles between subspaces spanned by the row-blocks of the reduced system are expected to be closer to orthogonal, and the reduced system is solved efficiently by the block conjugate gradient (CG) accelerated block Cimmino in fewer iterations. We also propose a novel metric for selecting “dense” columns considering the numerical values. The proposed metric establishes an upper bound on the sum of inner products between row-blocks. Then we propose an efficient algorithm for computing the proposed metric for the columns. Extensive numerical experiments for a wide range of linear systems confirm the effectiveness of the proposed scheme by achieving fewer iterations and faster parallel solution time compared to the classical CG accelerated block Cimmino algorithm.
Open Access
Fast and robust solution techniques for large scale linear least squares problems
(2020-07) Özaslan, İbrahim Kurban
Momentum Iterative Hessian Sketch (M-IHS) techniques, a group of solvers for large scale linear Least Squares (LS) problems, are proposed and analyzed in detail. Proposed M-IHS techniques are obtained by incorporating the Heavy Ball Acceleration into the Iterative Hessian Sketch algorithm and they provide signiﬁcant improvements over the randomized preconditioning techniques. By using approximate solvers along with the iterations, the proposed techniques are capable of avoiding all matrix decompositions and inversions, which is one of the main advantages over the alternative solvers such as the Blendenpik and the LSRN. Similar to the Chebyshev Semi-iterations, the M-IHS variants do not use any inner products and eliminate the corresponding synchronization steps in hierarchical or distributed memory systems, yet the M-IHS converges faster than the Chebyshev Semi-iteration based solvers. Lower bounds on the required sketch size for various randomized distributions are established through the error analyses of the M-IHS variants. Unlike the previously proposed approaches to produce a solution approximation, the proposed M-IHS techniques can use sketch sizes that are proportional to the statistical dimension which is always smaller than the rank of the coeﬃcient matrix. Additionally, hybrid schemes are introduced to estimate the unknown ℓ2-norm regularization parameter along with the iterations of the M-IHS techniques. Unlike conventional hybrid methods, the proposed Hybrid M-IHS techniques estimate the regularization parameter from the lower dimensional sub-problems that are constructed by random projections rather than the deterministic projections onto the Krylov Subspaces. Since the lower dimensional sub-problems that arise during the iterations of the Hybrid M-IHS variants are close approximations to the Newton sub-systems and the accuracy of their solutions increase exponentially, the parameters estimated from them rapidly converge to a proper regularization parameter for the full problem. In various numerical experiments conducted at several noise levels, the Hybrid M-IHS variants consistently estimated better regularization parameters and constructed solutions with less errors than the direct methods in far fewer iterations than the conventional hybrid methods. In large scale applications where the coeﬃcient matrix is distributed over a memory array, the proposed Hybrid M-IHS variants provide improved eﬃciency by minimizing the number of distributed matrix-vector multiplications with the coeﬃcient matrix.
Open Access
Image segmentation with unified region and boundary characteristics within recursive shortest spanning tree
(IEEE, 2007) Esen, E.; Alp, Yaşar Kemal
The lack of boundary information in region based image segmentation algorithms resulted in many hybrid methods that integrate the complementary information sources of region and boundary, in order to increase the segmentation performance. In compliance with this trend, we propose a novel method to unify the region and boundary characteristics within the canonical Recursive Shortest Spanning Tree algorithm. The main idea is to incorporate the boundary information in the distance metric of RSST with minor changes in the algorithm. Additionaly, we still benefit from the simple yet powerful structure of RSST. The results indicate the superiority of the proposed algorithm with respect to the conventional RSST. The object boundaries are successfully preserved. Therefore, the proposed algorithm is a candidate for video object segmentation where object boundaries coincide with motion field boundaries.
Open Access
Parallel direct and hybrid methods based on row block partitioning for solving sparse linear systems
(2017-08) Torun, Fahreddin Şükrü
Solving system of linear equations is a kernel operation in many scienti c and industrial applications. These applications usually give rise to linear systems in which the coe cient matrix is very large and sparse. The need for solving these large and sparse systems within a reasonable time necessitates e cient and e ective parallel solution methods. In this thesis, three novel approaches are proposed for reducing the parallel solution time of linear systems. First, a new parallel algorithm, ParBaMiN, is proposed in order to nd the minimum 2-norm solution of underdetermined linear systems, where the coe cient matrix is in the form of column overlapping block diagonal. The conducted experiments demonstrate the scalability of ParBaMiN on both shared and distributed memory architectures. Secondly, a new graph theoretical partitioning method is introduced in order to reduce the number of iterations in block Cimmino algorithm. Experimental results validate the e ectiveness of the proposed partitioning method in terms of reducing the required number of iterations. Finally, we propose a new parallel hybrid method, BCDcols, which further reduces the number of iterations of block Cimmino algorithm for matrices with dense columns. BCDcols combines the block Cimmino iterative algorithm and a dense direct method for solving the system. Experimental results show that BCDcols signi cantly improves the convergence rate of block Cimmino method and hence reduces the parallel solution time.