Clique Finder

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0

The Maximum Clique Problem (MCP) is a classical NP-hard problem that has gained considerable attention due to its numerous real-world applications and theoretical complexity. It is inherently computationally complex, and so exact methods may require prohibitive computing time. Nature-inspired meta-heuristics have proven their utility in solving many NP-hard problems. In this research, we propose a simulated annealing-based algorithm that we call Clique Finder algorithm to solve the MCP. Our algorithm uses a logarithmic cooling schedule and two moves that are selected in an adaptive manner. The objective (error) function is the total number of missing links in the clique, which is to be minimized. The proposed algorithm was evaluated using benchmark graphs from the open-source library DIMACS, and results show that the proposed algorithm had a high success rate.

2022 ◽  
Vol 13 (2) ◽  
pp. 1-22
Author(s):  
Sarab Almuhaideb ◽  
Najwa Altwaijry ◽  
Shahad AlMansour ◽  
Ashwaq AlMklafi ◽  
AlBandery Khalid AlMojel ◽  
...  

The Maximum Clique Problem (MCP) is a classical NP-hard problem that has gained considerable attention due to its numerous real-world applications and theoretical complexity. It is inherently computationally complex, and so exact methods may require prohibitive computing time. Nature-inspired meta-heuristics have proven their utility in solving many NP-hard problems. In this research, we propose a simulated annealing-based algorithm that we call Clique Finder algorithm to solve the MCP. Our algorithm uses a logarithmic cooling schedule and two moves that are selected in an adaptive manner. The objective (error) function is the total number of missing links in the clique, which is to be minimized. The proposed algorithm was evaluated using benchmark graphs from the open-source library DIMACS, and results show that the proposed algorithm had a high success rate.


Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.


2020 ◽  
Author(s):  
Shalin Shah

<p>A clique in a graph is a set of vertices that are all directly connected</p><p>to each other i.e. a complete sub-graph. A clique of the largest size is</p><p>called a maximum clique. Finding the maximum clique in a graph is an</p><p>NP-hard problem and it cannot be solved by an approximation algorithm</p><p>that returns a solution within a constant factor of the optimum. In this</p><p>work, we present a simple and very fast randomized algorithm for the</p><p>maximum clique problem. We also provide Java code of the algorithm</p><p>in our git repository. Results show that the algorithm is able to find</p><p>reasonably good solutions to some randomly chosen DIMACS benchmark</p><p>graphs. Rather than aiming for optimality, we aim to find good solutions</p><p>very fast.</p>


2020 ◽  
Author(s):  
Shalin Shah

<p>A clique in a graph is a set of vertices that are all directly connected</p><p>to each other i.e. a complete sub-graph. A clique of the largest size is</p><p>called a maximum clique. Finding the maximum clique in a graph is an</p><p>NP-hard problem and it cannot be solved by an approximation algorithm</p><p>that returns a solution within a constant factor of the optimum. In this</p><p>work, we present a simple and very fast randomized algorithm for the</p><p>maximum clique problem. We also provide Java code of the algorithm</p><p>in our git repository. Results show that the algorithm is able to find</p><p>reasonably good solutions to some randomly chosen DIMACS benchmark</p><p>graphs. Rather than aiming for optimality, we aim to find good solutions</p><p>very fast.</p>


2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Frode Eika Sandnes

The multimode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem. Particle swarm optimization (PSO) has been efficiently applied to the search for near optimal solutions to various NP-hard problems. MRCPSP involves solving two subproblems: mode assignment and activity priority determination. Hence, two PSOs are applied to each subproblem. A constriction PSO is proposed for the activity priority determination while a discrete PSO is employed for mode assignment. A least total resource usage (LTRU) heuristic and minimum slack (MSLK) heuristic ensure better initial solutions. To ensure a diverse initial collection of solutions and thereby enhancing the PSO efficiency, a best heuristic rate (HR) is suggested. Moreover, a new communication topology with random links is also introduced to prevent slow and premature convergence. To verify the performance of the approach, the MRCPSP benchmarks in PSPLIB were evaluated and the results compared to other state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms other algorithms for the MRCPSP problems. Finally, a real-world man-day project scheduling problem (MDPSP)—a MRCPSP problem—was evaluated and the results demonstrate that MDPSP can be solved successfully.


2012 ◽  
Vol 21 (5) ◽  
pp. 643-660 ◽  
Author(s):  
YONATAN BILU ◽  
NATHAN LINIAL

We introduce the notion of a stable instance for a discrete optimization problem, and argue that in many practical situations only sufficiently stable instances are of interest. The question then arises whether stable instances of NP-hard problems are easier to solve, and in particular, whether there exist algorithms that solve in polynomial time all sufficiently stable instances of some NP-hard problem. The paper focuses on the Max-Cut problem, for which we show that this is indeed the case.


2020 ◽  
Author(s):  
Shalin Shah

<p>A clique in a graph is a set of vertices that are all connected to each</p><p>other. A maximum clique is a clique of maximum size. A graph may have</p><p>more than one maximum cliques. The problem of finding a maximum</p><p>clique is a strongly hard NP-hard problem. It is not possible to find an</p><p>approximation algorithm which finds a maximum clique that is a constant</p><p>factor of the optimum solution. In this work, we present a genetic algorithm</p><p>for the maximum clique problem that is able to find optimum or</p><p>close to optimum solutions to most DIMACS graphs. The genetic algorithm</p><p>uses new crossover mechanisms which are able to find reasonably</p><p>good cliques which can then be used in other applications downstream.</p><p>We also provide C++ code for our algorithm. Results show that our algorithm</p><p>is able to find maximum cliques for most DIMACS instances, and</p><p>if not, close to optimum solutions for the other instances.</p>


2019 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Syafruddin Side ◽  
Maya Sari Wahyuni ◽  
Hadrianty Ramli

Abstrak. Warshall merupakan algoritma untuk menghitung jarak terpendek untuk semua pasangan titik pada sebuah lokasi yang dapat diubah menjadi sebuah graf berarah dan berbobot, yang berupa titik-titik (V) dan sisi-sisi (E) serta paling memiliki minimal satu sisi pada setiap titik. Vehicle Routing Problem (VRP) termasuk dalam kelas NP-hard problem dalam combinatorial optimization, sehingga sulit diselesaikan dengan metode eksak yang berlaku secara umum. Penelitian ini diawal dengan konsep matematis Penerapan Algoritma Warshall, yaitu pengambilan data Pendistribusian dari Perusahaan, pencarian bobot lintasan, mengubah kedalam matriks dengan ukuran  dalam hal ini matriks yang digunakan berukuran , menerapkan Algoritma Warshall dalam matriks yang diperoleh. Persamaan yang digunakan adalah pertama Representasi graf ke matriks berbobot berjarak D = [dij] yaitu jarak dari vertex i ke j; Kedua Dekomposisi dengan urutan dij(k). D(k) menjadi matriks nxn [dij(k)] batasi k sampai n sehingga k = 0, 1, …, n; Ketiga Pengamatan struktur shortest path dilakukan dengan dua cara yaitu jika k bukan merupakan vertex pada path (path terpendek memiliki panjang dij(k-1)) dan k merupakan vertex pada path (path terpendek memiliki panjang dij(k-1)+dij(k-1)), hal tersebut memuat sebuah subpath dari i ke k dan sebuah subpath dari k ke j. Keempat Iterasi yang dimulai dari 0 sampai dengan n. Berdasarkan hasil penelitian diperoleh bahwa dengan Metode Algoritma Warshall dapat menyelesaikan permasalahan penentuan rute terpendek dalam pendistribusian PT Semen Bosowa dengan menghitung jarak seluruh jalur lintasan yang ada dalam pendistribusian semen Bosowa di Makassar.Kata Kunci : Algoritma Warshall, Masalah Vehicle Routing, Graf Berarah, Graf Berbobot, Jalur Terpendek.Abstract  Warshall is an algorithm to calculate the shortest distance for every pair of points in a location that can be converted into a directed and weighted graph, in the form of vertex (V) and edges (E), and most have at least one side at any vertex. Vehicle Routing Problem (VRP) is included in the class of NP-hard problem in combinatorial optimization, making it difficult to solve with exact methods applicable in general. This study beginning with mathematical concepts Implementation of Algorithms Warshall, which is taking the data distribution from the Company, the search for weight trajectory, changing into a matrix with n × n squares in this case matrix used measuring 11 x 11, apply the algorithm Warshall in the matrix obtained, the second is the implementation of Algorithms Warshall using Microsoft Visual Basic programming language. The equation used is the first representation of the graph to a weighted matrix D = [dij] ie the distance from the vertex i to j; The second order decomposition with dij (k). D (k) be the nxn matrix [dij (k)] so that the limit k to n for k = 0, 1, ..., n; Third observation structures shortest path done in two ways: if k is not a vertex on the path (the shortest path length dij (k-1)) and k is the vertex on the path (the shortest path length dij (k-1) + dij (k -1)), it contains a subpath from i to k and a subpath from k to j. The fourth iteration numbered 0 through n. The result showed that the method Warshall algorithm can solve the problems of determining the shortest route in the distribution of PT Semen Bosowa by calculating the distance of the entire passage is in the distribution of cement Bosowa in Makassar.Keywords: Algorithm Warshall, Vehicle Routing Problem, trending Graf, Graf Weighted, Shortest Path.


2021 ◽  
Vol 30 (01) ◽  
pp. 2140004
Author(s):  
Shenshen Gu ◽  
Hanmei Yao

The maximum clique problem (MCP) is a famous NP-hard problem, which is difficult for the exact algorithm to solve when the dimension is large. In this paper, we applied the pointer network based method to solve this problem. First, we illustrated how to train the network with supervised learning strategy to obtain the solution to the maximum clique problem. We then further trained the pointer network with reinforcement learning strategy to obtain the vertices from the graph. For both strategies, backtracking algorithm is used to reselect the vertices. From the experimental results, we can see that both supervised learning and reinforcement learning work well. Promising results can be obtained up to 100 dimensions. This illustrates that the deep neural network based algorithms have great potentials for solving the maximum clique problem effectively and efficiently.


2020 ◽  
Author(s):  
Shalin Shah

<p>A clique in a graph is a set of vertices that are all connected to each</p><p>other. A maximum clique is a clique of maximum size. A graph may have</p><p>more than one maximum cliques. The problem of finding a maximum</p><p>clique is a strongly hard NP-hard problem. It is not possible to find an</p><p>approximation algorithm which finds a maximum clique that is a constant</p><p>factor of the optimum solution. In this work, we present a genetic algorithm</p><p>for the maximum clique problem that is able to find optimum or</p><p>close to optimum solutions to most DIMACS graphs. The genetic algorithm</p><p>uses new crossover mechanisms which are able to find reasonably</p><p>good cliques which can then be used in other applications downstream.</p><p>We also provide C++ code for our algorithm. Results show that our algorithm</p><p>is able to find maximum cliques for most DIMACS instances, and</p><p>if not, close to optimum solutions for the other instances.</p>


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