scholarly journals Scoring Functions Based on Second Level Score for k-SAT with Long Clauses

2014 ◽  
Vol 51 ◽  
pp. 413-441 ◽  
Author(s):  
S. Cai ◽  
C. Luo ◽  
K. Su
Keyword(s):  
Phase Transition ◽  
Computational Complexity ◽  
Local Search ◽  
State Of The Art ◽  
Scoring Function ◽  
Experimental Results ◽  
Great Success ◽  
Stochastic Local Search ◽  
Scoring Functions ◽  
New Scoring

It is widely acknowledged that stochastic local search (SLS) algorithms can efficiently find models for satisfiable instances of the satisfiability (SAT) problem, especially for random k-SAT instances. However, compared to random 3-SAT instances where SLS algorithms have shown great success, random k-SAT instances with long clauses remain very difficult. Recently, the notion of second level score, denoted as "score_2", was proposed for improving SLS algorithms on long-clause SAT instances, and was first used in the powerful CCASat solver as a tie breaker. In this paper, we propose three new scoring functions based on score_2. Despite their simplicity, these functions are very effective for solving random k-SAT with long clauses. The first function combines score and score_2, and the second one additionally integrates the diversification property "age". These two functions are used in developing a new SLS algorithm called CScoreSAT. Experimental results on large random 5-SAT and 7-SAT instances near phase transition show that CScoreSAT significantly outperforms previous SLS solvers. However, CScoreSAT cannot rival its competitors on random k-SAT instances at phase transition. We improve CScoreSAT for such instances by another scoring function which combines score_2 with age. The resulting algorithm HScoreSAT exhibits state-of-the-art performance on random k-SAT (k>3) instances at phase transition. We also study the computation of score_2, including its implementation and computational complexity.

scholarly journals RMalign: an RNA structural alignment tool based on a size independent scoring function

2018 ◽  
Author(s):  
Jinfang Zheng ◽  
Juan Xie ◽  
Xu Hong ◽  
Shiyong Liu
Keyword(s):  
Phase Transition ◽  
Rna Structure ◽  
State Of The Art ◽  
Structural Alignment ◽  
Complex Structure ◽  
3D Structure ◽  
Scoring Function ◽  
Structure Alignment ◽  
Rna Structures ◽  
Transition Points

ABSTRACTRNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein complex 3D structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases. Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.8771 and 0.7766, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structure similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Taking the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME.Author summaryRNA structures are important for RNA functions. With the increasing of RNA structures in PDB, RNA 3D structure alignment approaches have been developed. However, the scoring function which is used for measuring RNA structural similarity is still length dependent. This shortcoming limits its ability to detect RNA structure templates in modeling RNA structure or RNA-protein 3D complex structure. Thus, we developed a length independent scoring function RMscore to enhance the ability to detect RNA structure homologs. The benchmarking data shows that RMscore can distinct the similar and dissimilar RNA structure effectively. RMscore should be a useful scoring function in modeling RNA structures for the biological community. Based on RMscore, we develop an RNA 3D structure alignment RMalign. In both RNA structure and function classification benchmarking, RMalign obtains as good as or even better performance than the state-of-the-art approaches. With a length independent scoring function RMscore, RMalign should be useful for the modeling RNA structures. Based on above results, we update PRIME to PRIME2.0. We provide a more accurate RNA-protein 3D complex structure modeling tool PRIME2.0 which should be useful for the biological community.

scholarly journals A Dynamic Clause Specific Initial Weight Assignment for Solving Satisfiability Problems Using Local Search

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Abdelraouf Ishtaiwi ◽  
Feda Alshahwan ◽  
Naser Jamal ◽  
Wael Hadi ◽  
Muhammad AbuArqoub
Keyword(s):  
Local Search ◽  
State Of The Art ◽  
Small Neighborhood ◽  
Extensive Study ◽  
The State ◽  
Stochastic Local Search ◽  
Dynamic Allocation ◽  
Initial Weight ◽  
Large Neighborhood ◽  
Weight Assignment

For decades, the use of weights has proven its superior ability to improve dynamic local search weighting algorithms’ overall performance. This paper proposes a new mechanism where the initial clause’s weights are dynamically allocated based on the problem’s structure. The new mechanism starts by examining each clause in terms of its size and the extent of its link, and its proximity to other clauses. Based on our examination, we categorized the clauses into four categories: (1) clauses small in size and linked with a small neighborhood, (2) clauses small in size and linked with a large neighborhood, (3) clauses large in size and linked with a small neighborhood, and (4) clauses large in size and linked with a large neighborhood. Then, the initial weights are dynamically allocated according to each clause category. To examine the efficacy of the dynamic initial weight assignment, we conducted an extensive study of our new technique on many problems. The study concluded that the dynamic allocation of initial weights contributes significantly to improving the search process’s performance and quality. To further investigate the new mechanism’s effect, we compared the new mechanism with the state-of-the-art algorithms belonging to the same family in terms of using weights, and it was clear that the new mechanism outperformed the state-of-the-art clause weighting algorithms. We also show that the new mechanism could be generalized with minor changes to be utilized within the general-purpose stochastic local search state-of-the-art weighting algorithms.

scholarly journals Improved Local Search with Momentum for Bayesian Networks Structure Learning

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 750
Author(s):  
Xiaohan Liu ◽  
Xiaoguang Gao ◽  
Zidong Wang ◽  
Xinxin Ru
Keyword(s):  
Local Search ◽  
Complex Networks ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Hybrid Methods ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Local Optimum ◽  
Networks Structure

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

scholarly journals Extended Conjunctive Normal Form and An Efficient Algorithm for Cardinality Constraints

2020 ◽  
Author(s):  
Zhendong Lei ◽  
Shaowei Cai ◽  
Chuan Luo
Keyword(s):  
Normal Form ◽  
Local Search ◽  
State Of The Art ◽  
Scoring Function ◽  
Conjunctive Normal Form ◽  
Discrete Tomography ◽  
Cardinality Constraint ◽  
Cardinality Constraints ◽  
Important Constraint ◽  
Unit Propagation

Satisfiability (SAT) and Maximum Satisfiability (MaxSAT) are two basic and important constraint problems with many important applications. SAT and MaxSAT are expressed in CNF, which is difficult to deal with cardinality constraints. In this paper, we introduce Extended Conjunctive Normal Form (ECNF), which expresses cardinality constraints straightforward and does not need auxiliary variables or clauses. Then, we develop a simple and efficient local search solver LS-ECNF with a well designed scoring function under ECNF. We also develop a generalized Unit Propagation (UP) based algorithm to generate the initial solution for local search. We encode instances from Nurse Rostering and Discrete Tomography Problems into CNF with three different cardinality constraint encodings and ECNF respectively. Experimental results show that LS-ECNF has much better performance than state of the art MaxSAT, SAT, Pseudo-Boolean and ILP solvers, which indicates solving cardinality constraints with ECNF is promising.

scholarly journals Improving WalkSAT for Random 3-SAT Problems

2020 ◽  
Vol 26 (2) ◽  
pp. 220-243
Author(s):  
Huimin Fu ◽  
Yang Xu ◽  
Shuwei Chen ◽  
Jun Liu
Keyword(s):  
Phase Transition ◽  
Ant Colony Algorithm ◽  
State Of The Art ◽  
Stochastic Local Search ◽  
Improved Genetic Algorithm ◽  
Initial Assignment ◽  
Sat Solver ◽  
Random Instances ◽  
Improved Ant Colony Algorithm ◽  
Better Than

Stochastic local search (SLS) algorithms are well known for their ability to efficiently find models of random instances of the Boolean satisfiability (SAT) problems. One of the most famous SLS algorithms for SAT is called WalkSAT, which has wide influence and performs well on most of random 3-SAT instances. However, the performance of WalkSAT lags far behind on random 3-SAT instances equal to or greater than the phase transition ratio. Motivated by this limitation, in the present work, firstly an allocation strategy is introduced and utilized in WalkSAT to determine the initial assignment, leading to a new algorithm called WalkSATvav. The experimental results show that WalkSATvav significantly outperforms the state-of-the-art SLS solvers on random 3-SAT instances at the phase transition for SAT Competition 2017. However, WalkSATvav cannot rival its competitors on random 3-SAT instances greater than the phase transition ratio. Accordingly, WalkSATvav is further improved for such instances by utilizing a combination of an improved genetic algorithm and an improved ant colony algorithm, which complement each other in guiding the search direction. The resulting algorithm, called WalkSATga, is far better than WalkSAT and significantly outperforms some previous known SLS solvers on random 3-SAT instances greater than the phase transition ratio from SAT Competition 2017. Finally, a new SAT solver called WalkSATlg, which combines WalkSATvav and WalkSATga, is proposed, which is competitive with the winner of random satisfiable category of SAT competition 2017 on random 3-SAT problem.

scholarly journals Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation

2017 ◽  
Author(s):  
Yi Fan ◽  
Nan Li ◽  
Chengqian Li ◽  
Zongjie Ma ◽  
Longin Jan Latecki ◽  
...  
Keyword(s):  
Random Walk ◽  
Local Search ◽  
Real World ◽  
State Of The Art ◽  
Real Data ◽  
Experimental Results ◽  
Data Sets ◽  
Vertex Weight ◽  
Real World Applications ◽  
Clustering Aggregation

The Maximum Vertex Weight Clique (MVWC) problem is NP-hard and also important in real-world applications. In this paper we propose to use the restart and the random walk strategies to improve local search for MVWC. If a solution is revisited in some particular situation, the search will restart. In addition, when the local search has no other options except dropping vertices, it will use random walk. Experimental results show that our solver outperforms state-of-the-art solvers in DIMACS and finds a new best-known solution. Also it is the unique solver which is comparable with state-of-the-art methods on both BHOSLIB and large crafted graphs. Furthermore we evaluated our solver in clustering aggregation. Experimental results on a number of real data sets demonstrate that our solver outperforms the state-of-the-art for solving the derived MVWC problem and helps improve the final clustering results.

scholarly journals Image Denoising Based on Improved Hybrid Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
pp. 14-21
Author(s):  
Nail Alaoui ◽  
Amel Baha Houda Adamou-Mitiche ◽  
Lahcène Mitiche ◽  
Lakhdar Bouhamla
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Image Denoising ◽  
Scientific Community ◽  
Digital Images ◽  
State Of The Art ◽  
Hybrid Genetic Algorithm ◽  
Experimental Results ◽  
Mutation Operators ◽  
Fundamental Challenge

Digital images can be degraded through noise during the transmission and process of acquisition, it is still a fundamental challenge is to eliminate as much noise as possible while preserving the main features of the image, for instance, edges, texture, and corners. This paper proposes for image denoising a new Improved Hybrid Genetic Algorithm (IHGA), whose combined a Genetic Algorithm (GA), with some image denoising methods. Wherein this approach uses mutation operators, crossover, and population reinitialization as default operators available in evolutionary methods with applied some state-of-the-art image denoising methods, such as local search. Tests are conducted on some digital images, commonly used as a benchmark by the scientific community, where different standard deviations are used for digital images. Experimental results indicate that the proposed method is very effective and competitive in comparison with previously published works.

scholarly journals Stochastic local search: a state-of-the-art review

2021 ◽  
Vol 11 (1) ◽  
pp. 716
Author(s):  
Muhamet Kastrati ◽  
Marenglen Biba
Keyword(s):  
Local Search ◽  
State Of The Art ◽  
Search Algorithms ◽  
Combinatorial Problems ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Science And Engineering ◽  
Short Introduction ◽  
Basic Notation ◽  
Learning Data

The main objective of this paper is to provide a state-of-the-art review, analyze and discuss stochastic local search techniques used for solving hard combinatorial problems. It begins with a short introduction, motivation and some basic notation on combinatorial problems, search paradigms and other relevant features of searching techniques as needed for background. In the following a brief overview of the stochastic local search methods along with an analysis of the state-of-the-art stochastic local search algorithms is given. Finally, the last part of the paper present and discuss some of the most latest trends in application of stochastic local search algorithms in machine learning, data mining and some other areas of science and engineering. We conclude with a discussion on capabilities and limitations of stochastic local search algorithms.

scholarly journals Nonoverlapping Blocks Based Copy-Move Forgery Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Sun ◽  
Rongrong Ni ◽  
Yao Zhao
Keyword(s):  
Computational Complexity ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Smooth Part ◽  
Block Based ◽  
Copy Move Forgery Detection ◽  
High Computational Complexity

In order to solve the problem of high computational complexity in block-based methods for copy-move forgery detection, we divide image into texture part and smooth part to deal with them separately. Keypoints are extracted and matched in texture regions. Instead of using all the overlapping blocks, we use nonoverlapping blocks as candidates in smooth regions. Clustering blocks with similar color into a group can be regarded as a preprocessing operation. To avoid mismatching due to misalignment, we update candidate blocks by registration before projecting them into hash space. In this way, we can reduce computational complexity and improve the accuracy of matching at the same time. Experimental results show that the proposed method achieves better performance via comparing with the state-of-the-art copy-move forgery detection algorithms and exhibits robustness against JPEG compression, rotation, and scaling.

scholarly journals SneakySnake: a fast and accurate universal genome pre-alignment filter for CPUs, GPUs and FPGAs

2020 ◽  
Author(s):  
Mohammed Alser ◽  
Taha Shahroodi ◽  
Juan Gómez-Luna ◽  
Can Alkan ◽  
Onur Mutlu
Keyword(s):  
Sequence Alignment ◽  
State Of The Art ◽  
Optimal Path ◽  
Scoring Function ◽  
Supplementary Information ◽  
Gpu Acceleration ◽  
Scoring Functions ◽  
Vlsi Chip ◽  
Bit Vector ◽  
Grid Layout

Abstract Motivation We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout. In the SNR problem, we are interested in finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles. The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether or not performing sequence alignment is necessary. Reducing the ASM problem into SNR also makes SneakySnake efficient to implement on CPUs, GPUs and FPGAs. Results SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper and SHD. For short sequences, SneakySnake accelerates Edlib (state-of-the-art implementation of Myers’s bit-vector algorithm) and Parasail (state-of-the-art sequence aligner with a configurable scoring function), by up to 37.7× and 43.9× (>12× on average), respectively, with its CPU implementation, and by up to 413× and 689× (>400× on average), respectively, with FPGA and GPU acceleration. For long sequences, the CPU implementation of SneakySnake accelerates Parasail and KSW2 (sequence aligner of minimap2) by up to 979× (276.9× on average) and 91.7× (31.7× on average), respectively. As SneakySnake does not replace sequence alignment, users can still obtain all capabilities (e.g. configurable scoring functions) of the aligner of their choice, unlike existing acceleration efforts that sacrifice some aligner capabilities. Availabilityand implementation https://github.com/CMU-SAFARI/SneakySnake. Supplementary information Supplementary data are available at Bioinformatics online.

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