Bipartite Matching Algorithm Research Articles

3D object retrieval with multi-feature collaboration and bipartite graph matching

In this paper, we propose a novel 3D object retrieval with features collaboration and bipartite graph matching strategies. We explored the essential characters of 3D object in a view-based retrieval framework, which extracts complement descriptors from both the contour and the interior region of 3D object effectively. Specifically, a greedy bipartite graph matching algorithm is employed. With the bipartite graph matching and feature concatenation, significant performance improvement is achieved in the 3D object retrieval task. The proposed method is evaluated by the third party on the data set comprising more than 500 3D objects and achieves the best performance for SHREC’15 challenge.

Expected time complexity of the auction algorithm and the push relabel algorithm for maximum bipartite matching on random graphs

In this paper we analyze the expected time complexity of the auction algorithm for the matching problem on random bipartite graphs. We first prove that if for every non-maximum matching on graph G there exist an augmenting path with a length of at most 2l + 1 then the auction algorithm converges after N ⋅ l iterations at most. Then, we prove that the expected time complexity of the auction algorithm for bipartite matching on random graphs with edge probability and c > 1 is w.h.p. This time complexity is equal to other augmenting path algorithms such as the HK algorithm. Furthermore, we show that the algorithm can be implemented on parallel machines with processors and shared memory with an expected time complexity of . © 2014 Wiley Periodicals, Inc. Random Struct. Alg., 48, 384–395, 2016

EXTENDED COMPATIBILITY PATH BASED HARDWARE BINDING: AN ADAPTIVE ALGORITHM FOR HIGH LEVEL SYNTHESIS OF AREA-TIME EFFICIENT DESIGNS

Hardware binding is an important step in high level synthesis (HLS). The quality of hardware binding affects the area-time efficiency of a design. The goal of a synthesis process is to produce a design which meets the area-time requirements. In this paper, we present a new hardware binding algorithm with focus on area reduction. It is called extended compatibility path-based (ECPB) hardware binding and extends the compatibility path-based (CPB) hardware binding method by exploiting inter-operation flow dependencies, non-overlapping lifetimes of variables and modifying the weight relation in order to make it application aware and thus adaptive in nature. The presented methodology also takes into account bit width of functional units (FUs) and multi mode FUs. It performs simultaneous FU and register binding. Implemented within a C to register transfer level (RTL) framework, it produces binding results which are better than those produced by weighted bipartite matching (WBM) and CPB algorithms. The use of ECPB algorithm results in an average reduction of 34% and 17.44% in area-time product over WBM and CPB methods, respectively.

Online Stochastic Matching: New Algorithms with Better Bounds

We consider variants of the online stochastic bipartite matching problem motivated by Internet advertising display applications, as introduced in Feldman et al. [Feldman J, Mehta A, Mirrokni VS, Muthukrishnan S (2009) Online stochastic matching: Beating 1 − 1/e. FOCS '09: Proc. 50th Annual IEEE Sympos. Foundations Comput. Sci. (IEEE, Washington, DC), 117–126]. In this setting, advertisers express specific interests into requests for impressions of different types. Advertisers are fixed and known in advance, whereas requests for impressions come online. The task is to assign each request to an interested advertiser (or to discard it) immediately upon its arrival. In the adversarial online model, the ranking algorithm of Karp et al. [Karp RM, Vazirani UV, Varirani VV (1990) An optimal algorithm for online bipartite matching. STOC '90: Proc. 22nd Annual ACM Sympos. Theory Comput. (ACM, New York), 352–358] provides a best possible randomized algorithm with competitive ratio 1 − 1/e ≈ 0.632. In the stochastic i.i.d. model, when requests are drawn repeatedly and independently from a known probability distribution over the different impression types, Feldman et al. [Feldman J, Mehta A, Mirrokni VS, Muthukrishnan S (2009) Online stochastic matching: Beating 1 − 1/e. FOCS '09: Proc. 50th Annual IEEE Sympos. Foundations Comput. Sci. (IEEE, Washington, DC), 117–126] prove that one can do better than 1 − 1/e. Under the restriction that the expected number of request of each impression type is an integer, they provide a 0.670-competitive algorithm, later improved by Bahmani and Kapralov [Bahmani B, Kapralov M (2010) Improved bounds for online stochastic matching. ESA '10: Proc. 22nd Annual Eur. Sympos. Algorithms (Springer-Verlag, Berlin, Heidelberg), 170–181] to 0.699 and by Manshadi et al. [Manshadi V, Gharan SO, Saberi A (2012) Online stochastic matching: Online actions based on offline statistics. Math. Oper. Res. 37(4):559–573] to 0.705. Without this integrality restriction, Manshadi et al. are able to provide a 0.702-competitive algorithm. In this paper we consider a general class of online algorithms for the i.i.d. model that improve on all these bounds and that use computationally efficient offline procedures (based on the solution of simple linear programs of maximum flow types). Under the integrality restriction on the expected number of impression types, we get a 1 − 2e−2(≈0.729)-competitive algorithm. Without this restriction, we get a 0.706-competitive algorithm. Our techniques can also be applied to other related problems such as the online stochastic vertex-weighted bipartite matching problem as defined in Aggarwal et al. [Aggarwal G, Goel G, Karande C, Mehta A (2011) Online vertex-weighted bipartite matching and single-bid budgeted allocations. SODA '11: Proc. 22nd Annual ACM-SIAM Sympos. Discrete Algorithms (SIAM, Philadelphia), 1253–1264]. For this problem, we obtain a 0.725-competitive algorithm under the stochastic i.i.d. model with integral arrival rate. Finally, we show the validity of all our results under a Poisson arrival model, removing the need to assume that the total number of arrivals is fixed and known in advance, as is required for the analysis of the stochastic i.i.d. models described above.

Implicit computation of maximum bipartite matchings by sublinear functional operations

The maximum bipartite matching problem, an important problem in combinatorial optimization, has been studied for a long time. In order to solve problems for very large structured graphs in reasonable time and space, implicit algorithms have been investigated. Any object to be manipulated is binary encoded and problems have to be solved mainly by functional operations on the corresponding Boolean functions. OBDDs are a popular data structure for Boolean functions, therefore, OBDD-based algorithms have been used as a heuristic approach to handle large input graphs. Here, two OBDD-based maximum bipartite matching algorithms are presented, which are the first ones using only a sublinear number of operations (with respect to the number of vertices of the input graph) for a problem unknown to be in NC, the complexity class that contains all problems computable in deterministic polylogarithmic time with polynomially many processors. Furthermore, the algorithms are experimentally evaluated.

A case study for understanding the nature of redundant entities in bibliographic digital libraries

Purpose – The purpose of this paper is to collect and understand the nature of real cases of author name variants that have often appeared in bibliographic digital libraries (DLs) as a case study of the name authority control problem in DLs. Design/methodology/approach – To find a sample of name variants across DLs (e.g. DBLP and ACM) and in a single DL (e.g. ACM), the approach is based on two bipartite matching algorithms: Maximum Weighted Bipartite Matching and Maximum Cardinality Bipartite Matching. Findings – First, the authors validated the effectiveness and efficiency of the bipartite matching algorithms. The authors also studied the nature of real cases of author name variants that had been found across DLs (e.g. ACM, CiteSeer and DBLP) and in a single DL. Originality/value – To the best of the authors knowledge, there is less research effort to understand the nature of author name variants shown in DLs. A thorough analysis can help focus research effort on real problems that arise when the authors perform duplicate detection methods.

TRACTABLE AND INTRACTABLE VARIATIONS OF UNORDERED TREE EDIT DISTANCE

In this paper, we investigate the problem of computing structural sensitive variations of an unordered tree edit distance. First, we focus on the variations tractable by the algorithms including the submodule of a network algorithm, either the minimum cost maximum flow algorithm or the maximum weighted bipartite matching algorithm. Then, we show that both network algorithms are replaceable, and hence the time complexity of computing these variations can be reduced to O(nmd) time, where n is the number of nodes in a tree, m is the number of nodes in another tree and d is the minimum degree of given two trees. Next, we show that the problem of computing the bottom-up distance is MAX SNP-hard. Note that the well-known linear-time algorithm for the bottom-up distance designed by Valiente (2001) computes just a bottom-up indel (insertion-deletion) distance allowing no substitutions.

On parallel push–relabel based algorithms for bipartite maximum matching

We study multithreaded push–relabel based algorithms for computing maximum cardinality matching in bipartite graphs. Matching is a fundamental combinatorial problem with applications in a wide variety of problems in science and engineering. We are motivated by its use in the context of sparse linear solvers for computing the maximum transversal of a matrix. Other applications can be found in many fields such as bioinformatics (Azad et al., 2010) [4], scheduling (Timmer and Jess, 1995) [27], and chemical structure analysis (John, 1995) [14]. We implement and test our algorithms on several multi-socket multicore systems and compare their performance to state-of-the-art augmenting path-based serial and parallel algorithms using a test set comprised of a wide range of real-world instances. Building on several heuristics for enhancing performance, we demonstrate good scaling for the parallel push–relabel algorithm. We show that it is comparable to the best augmenting path-based algorithms for bipartite matching. To the best of our knowledge, this is the first extensive study of multithreaded push–relabel based algorithms. In addition to a direct impact on the applications using matching, the proposed algorithmic techniques can be extended to preflow-push based algorithms for computing maximum flow in graphs.

The Extended Algorithm of the Bipartite Graph Matching Semantic Web Service Discovery

In previous work, the ontology-based algorithm of bipartite graph matching semantic web service discovery was presented. But in these algorithms, the problem of finding augment path was not resolved very well. It will affect the recall rate and precision rate of web service discovery. In order to resolve these problems, this paper proposes an ontology-based bipartite graph matching semantic web service discovery algorithm, which extends the optimal bipartite graph matching algorithm. It uses the slack function to find the augment path in the equivalent sub graph. The algorithm also contributes to the improvement of the recall rate and precision rate of the service discovery.

Ensuring cache reliability and energy scaling at near-threshold voltage with Macho

Nanoscale process variations in conventional SRAM cells are known to limit voltage scaling in microprocessor caches. Recently, a number of novel cache architectures have been proposed which substitute faulty words of one cache line with healthy words of others, to tolerate these failures at low voltages. These schemes rely on the fault maps to identify faulty words, inevitably increasing the chip area. Besides, the relationship between word sizes and the cache failure rates is not well studied in these works. In this paper, we analyze the word substitution schemes by employing Fault Tree Model and Collision Graph Model. A novel cache architecture (Macho) is then proposed based on this model. Macho is dynamically reconfigurable and is locally optimized (tailored to local fault density) using two algorithms: 1) a graph coloring algorithm for moderate fault densities and 2) a bipartite matching algorithm to support high fault densities. An adaptive matching algorithm enables on-demand reconfiguration of Macho to concentrate available resources on cache working sets. As a result, voltage scaling down to 400 mV is possible, tolerating bit failure rates reaching 1 percent (one failure in every 100 cells). This near-threshold voltage (NTV) operation achieves 44 percent energy reduction in our simulated system (CPU $+$ DRAM models) with a 1 MB L2 cache.

New approaches to multi-objective optimization

A natural way to deal with multiple, partially conflicting objectives is turning all the objectives but one into budget constraints. Many classical optimization problems, such as maximum spanning tree and forest, shortest path, maximum weight (perfect) matching, maximum weight independent set (basis) in a matroid or in the intersection of two matroids, become NP-hard even with one budget constraint. Still, for most of these problems efficient deterministic and randomized approximation schemes are known. Not much is known however about the case of two or more budgets: filling this gap, at least partially, is the main goal of this paper. In more detail, we obtain the following main results: Using iterative rounding for the first time in multi-objective optimization, we obtain multi-criteria PTASs (which slightly violate the budget constraints) for spanning tree, matroid basis, and bipartite matching with $$k=O(1)$$ budget constraints. We present a simple mechanism to transform multi-criteria approximation schemes into pure approximation schemes for problems whose feasible solutions define an independence system. This gives improved algorithms for several problems. In particular, this mechanism can be applied to the above bipartite matching algorithm, hence obtaining a pure PTAS. We show that points in low-dimensional faces of any matroid polytope are almost integral, an interesting result on its own. This gives a deterministic approximation scheme for $$k$$ -budgeted matroid independent set. We present a deterministic approximation scheme for $$k$$ -budgeted matching (in general graphs), where $$k=O(1)$$ . Interestingly, to show that our procedure works, we rely on a non-constructive result by Stromquist and Woodall, which is based on the Ham Sandwich Theorem.

Ligand Binding Site Similarity Identification Based on Chemical and Geometric Similarity

The similarity comparison of binding sites based on amino acid between different proteins can facilitate protein function identification. However, Binding site usually consists of several crucial amino acids which are frequently dispersed among different regions of a protein and consequently make the comparison of binding sites difficult. In this study, we introduce a new method, named as chemical and geometric similarity of binding site (CGS-BSite), to compute the ligand binding site similarity based on discrete amino acids with maximum-weight bipartite matching algorithm. The principle of computing the similarity is to find a Euclidean Transformation which makes the similar amino acids approximate to each other in a geometry space, and vice versa. CGS-BSite permits site and ligand flexibilities, provides a stable prediction performance on the flexible ligand binding sites. Binding site prediction on three test datasets with CGS-BSite method has similar performance to Patch-Surfer method but outperforms other five tested methods, reaching to 0.80, 0.71 and 0.85 based on the area under the receiver operating characteristic curve, respectively. It performs a marginally better than Patch-Surfer on the binding sites with small volume and higher hydrophobicity, and presents good robustness to the variance of the volume and hydrophobicity of ligand binding sites. Overall, our method provides an alternative approach to compute the ligand binding site similarity and predict potential special ligand binding sites from the existing ligand targets based on the target similarity.

A fast approach to global alignment of protein-protein interaction networks.

BackgroundGlobal network alignment has been proposed as an effective tool for computing functional orthology. Commonly used global alignment techniques such as IsoRank rely on a two-step process: the first step is an iterative diffusion-based approach for assigning similarity scores to all possible node pairs (matchings); the second step applies a maximum-weight bipartite matching algorithm to this similarity score matrix to identify orthologous node pairs. While demonstrably successful in identifying orthologies beyond those based on sequences, this two-step process is computationally expensive. Recent work on computation of node-pair similarity matrices has demonstrated that the computational cost of the first step can be significantly reduced. The use of these accelerated methods renders the bipartite matching step as the dominant computational cost. This motivates a critical assessment of the tradeoffs of computational cost and solution quality (matching quality, topological matches, and biological significance) associated with the bipartite matching step. In this paper we utilize the state-of-the-art core diffusion-based step in IsoRank for similarity matrix computation, and couple it with two heuristic bipartite matching algorithms – a matrix-based greedy approach, and a tunable, adaptive, auction-based matching algorithm developed by us. We then compare our implementations against the performance and quality characteristics of the solution produced by the reference IsoRank binary, which also implements an optimal matching algorithm.ResultsUsing heuristic matching algorithms in the IsoRank pipeline exhibits dramatic speedup improvements; typically ×30 times faster for the total alignment process in most cases of interest. More surprisingly, these improvements in compute times are typically accompanied by better or comparable topological and biological quality for the network alignments generated. These measures are quantified by the number of conserved edges in the alignment graph, the percentage of enriched components, and the total number of covered Gene Ontology (GO) terms.ConclusionsWe have demonstrated significant reductions in global network alignment computation times by coupling heuristic bipartite matching methods with the similarity scoring step of the IsoRank procedure. Our heuristic matching techniques maintain comparable – if not better – quality in resulting alignments. A consequence of our work is that network-alignment based orthologies can be computed within minutes (as compared to hours) on typical protein interaction networks, enabling a more comprehensive tuning of alignment parameters for refined orthologies.

Efficient Loop Scheduling for Chip Multiprocessors with Non-Volatile Main Memory

Non-volatile memories (NVMs) show great potential in replacing DRAM as the main memory in many embedded systems because of their attractive characteristics such as low cost, high density, and low energy consumption. However, the problem of asymmetric read and write costs has to be addressed before the advantages of NVM can be fully exploited. That is, the cost of write operation is much more expensive than the cost of read operation on NVMs. The existing techniques for loop optimization cannot be used effectively with non-volatile main memory because this special feature is not considered. In this paper, we propose an efficient loop scheduling algorithm, the Rotation with Maximum Bipartite Matching (RMBM) algorithm, to address the problem of expensive write operations on non-volatile main memory for chip multiprocessors (CMPs). It achieves high parallelism for a loop and, at the same time, reduces the number of write operations on NVM. The experimental results show that the RMBM algorithm reduces the number of write activities on NVM by 34.5 % on average compared with the traditional rotation scheduling algorithm. The execution time is reduced by 20.5 %, and the energy consumption is also reduced by 15.03 % on average using the RMBM algorithm. In other words, the average lifetime of NVM can be extended by more than 2 times using the proposed technique.

Real-Time Path Planning for Coordinated Transport of Multiple Particles Using Optical Tweezers

Automated transport of multiple particles using optical tweezers requires real-time path planning to move them in coordination by avoiding collisions among themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized path planning approach by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. We use an iterative version of a maximum bipartite graph matching algorithm to assign given goal locations to such particles. We then employ a three-step method consisting of clustering, classification, and branch and bound optimization to determine the final collision-free paths. We demonstrate the effectiveness of the developed approach via experiments using silica beads in a holographic tweezers setup. We also discuss the applicability of our approach and challenges in manipulating biological cells indirectly by using the transported particles as grippers.

Detecting local ligand‐binding site similarity in nonhomologous proteins by surface patch comparison

Functional elucidation of proteins is one of the essential tasks in biology. Function of a protein, specifically, small ligand molecules that bind to a protein, can be predicted by finding similar local surface regions in binding sites of known proteins. Here, we developed an alignment free local surface comparison method for predicting a ligand molecule which binds to a query protein. The algorithm, named Patch-Surfer, represents a binding pocket as a combination of segmented surface patches, each of which is characterized by its geometrical shape, the electrostatic potential, the hydrophobicity, and the concaveness. Representing a pocket by a set of patches is effective to absorb difference of global pocket shape while capturing local similarity of pockets. The shape and the physicochemical properties of surface patches are represented using the 3D Zernike descriptor, which is a series expansion of mathematical 3D function. Two pockets are compared using a modified weighted bipartite matching algorithm, which matches similar patches from the two pockets. Patch-Surfer was benchmarked on three datasets, which consist in total of 390 proteins that bind to one of 21 ligands. Patch-Surfer showed superior performance to existing methods including a global pocket comparison method, Pocket-Surfer, which we have previously introduced. Particularly, as intended, the accuracy showed large improvement for flexible ligand molecules, which bind to pockets in different conformations.

Ontology mapping using bipartite graph

Fast improvement of web technologies have caused a problem of semantic integration between distributed applications. In this respect, sharing and distribution of information are of vital importance for ontologies. Ontologies may be improved by any independent association and still be used by another association. In case an association decides to use an ontology improved by another association, they should make mapping between ontology concepts. Different worldviews assign different meanings to different concepts and defines them differently. Therefore, mapping stands as an inevitable process. Mapping is the job of finding objects that are compatible between two ontologies. Semantic mapping of ontologies by means of bipartite graph matching algorithms has been studied in this paper. A mapping system defined as has been improved. We have named this system BGOM (bipartite graph optimal mapping). BGOM system finds the one-to-one matching between ontologies and which are in similar domains or in same domain. Firstly, two data matrices for ontology concepts and have been obtained. Next, a score matrix has been obtained from general data matrixes by using Levensthein metric. Finally, Kuhn-Munkres optimal assignment algorithm has been used to optimally map the concepts between and. The reason for this is to find one-to-one matches of concepts in the model we have improved. Kuhn-Munkres algorithm is an effective way to find the most similar couples (Tassa, 2007). Consequently, a one-to-one optimal map has been obtained between source and target ontologies. Application, prediction capability and truth values of BGOM system is evaluated by ontology alignment evaluation initiative (OAEI1) and satisfactory results have been obtained. Precision, recall and f-measure values of alignment results in the system we have improved, and are compared to the other systems in OAEI campaign and considerably good results have been obtained. Application of BGOM system between source and target ontologies has assisted effectively for solution of ontology mapping problem. Key words: Ontology, bipartite graph, ontology mapping, Levensthein metric, Kuhn-Munkres optimal assignment algorithm

Local Protein Surface Patch Method for Protein-Ligand Binding Prediction

Characterizing the protein structures of an unknown function is an important task in bioinformatics. Function of a protein, specifically, the type of ligand that bind to a protein, can be predicted by finding similar local surface regions of known proteins. Unlike existing methods that compare the global characteristics of a protein fold, we propose a local surface-based approach which can find functional similarity between non-homologous proteins. In the proposed pocket comparison method, the pockets are segmented to surface patches, which are then compared using a modified weighted bipartite matching algorithm. The 3D Zernike descriptors, which have been found to be successful in representing protein global surface properties (Sael L, Li B et al. Proteins, 2008; Sael L, La D et al. Proteins, 2008) and global pockets shape comparison (Chikhi R et al. Proteins, 2010), are used to encode the geometric and physicochemical properties of the surface patches. By representing a pocket by a set of local patches, local similarity of binding pockets can be captured. This is effective when pocket shapes are slightly different due to flexibility of ligand molecules. The binding ligand prediction performance was evaluated on a data set of 100 non-homologous proteins that bind to either one of nine types of ligands. 84.0% of the binding ligands were predicted correctly within the top three scores using the shape and pocket size information, which is better than the previous method which uses the surface of whole pocket. The performance was further improved to 87.0% when surface properties, i.e. electrostatic potential and hydrophobicity, were added. Overall, we show that proposed method is powerful in predicting the type of ligand a protein binds even in the absence of homologous proteins in the database.

System Combination Based on WSD Using WordNet

摘要: 最近混淆网络在融合多个机器翻译结果中展示很好的性能. 然而为了克服在不同的翻译系统中不同的词序, 假设对齐在混淆网络的构建上仍然是一个重要的问题. 但以往的对齐方法都没有考虑到语义信息. 本文为了更好地改进系统融合的性能, 提出了用词义消歧(Word sense disambiguation, WSD)来指导混淆网络中的对齐. 同时骨架翻译的选择也是通过计算句子间的相似度来获得的, 句子的相似性计算使用了二分图的最大匹配算法. 为了使得基于WordNet词义消歧方法融入到系统中, 本文将翻译错误率(Translation error rate, TER)算法进行了改进, 实验结果显示本方法的性能好于经典的TER算法的性能. 关键词: 系统融合 / 翻译错误率 / 词义消歧 / 混淆网络

Binding ligand prediction for proteins using partial matching of local surface patches.

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.