Maximal subset

Maximal subset

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It only takes a minute to sign up. As for your other question, orthogonal vectors are perpendicular. Orthogonality extends this notion of perpendicular to higher dimensions. This is by no means a proof. Given several non-zero vectors, if they are orthogonal one to anotherthen they are linearly independent. Moreover, every euclidian space has an orthonormal basis.

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Related 5. Hot Network Questions. Question feed. Mathematics Stack Exchange works best with JavaScript enabled.Cliques are one of the basic concepts of graph theory and are used in many other mathematical problems and constructions on graphs.

Cliques have also been studied in computer science : the task of finding whether there is a clique of a given size in a graph the clique problem is NP-completebut despite this hardness result, many algorithms for finding cliques have been studied. Cliques have many other applications in the sciences and particularly in bioinformatics.

This is equivalent to the condition that the induced subgraph of G induced by C is a complete graph. In some cases, the term clique may also refer to the subgraph directly.

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A maximal clique is a clique that cannot be extended by including one more adjacent vertex, that is, a clique which does not exist exclusively within the vertex set of a larger clique. Some authors define cliques in a way that requires them to be maximal, and use other terminology for complete subgraphs that are not maximal. A maximum clique of a graph, Gis a clique, such that there is no clique with more vertices.

The clique cover number of a graph G is the smallest number of cliques of G whose union covers the set of vertices V of the graph. A maximum clique transversal of a graph is a subset of vertices with the property that each maximum clique of the graph contains at least one vertex in the subset. The opposite of a clique is an independent setin the sense that every clique corresponds to an independent set in the complement graph.

The clique cover problem concerns finding as few cliques as possible that include every vertex in the graph. A related concept is a bicliquea complete bipartite subgraph. The bipartite dimension of a graph is the minimum number of bicliques needed to cover all the edges of the graph. Closely related concepts to complete subgraphs are subdivisions of complete graphs and complete graph minors.

In particular, Kuratowski's theorem and Wagner's theorem characterize planar graphs by forbidden complete and complete bipartite subdivisions and minors, respectively. In computer sciencethe clique problem is the computational problem of finding a maximum clique, or all cliques, in a given graph.

Nevertheless, many algorithms for computing cliques have been developed, either running in exponential time such as the Bron—Kerbosch algorithm or specialized to graph families such as planar graphs or perfect graphs for which the problem can be solved in polynomial time.

The same definition was used by Festinger in an article using less technical terms. Both works deal with uncovering cliques in a social network using matrices. For continued efforts to model social cliques graph-theoretically, see e. Many different problems from bioinformatics have been modeled using cliques. Sugihara uses cliques to model ecological niches in food webs. Power graph analysis is a method for simplifying complex biological networks by finding cliques and related structures in these networks.

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Cliques have also been used in automatic test pattern generation : a large clique in an incompatibility graph of possible faults provides a lower bound on the size of a test set. In chemistryRhodes et al.

From Wikipedia, the free encyclopedia. Redirected from Maximal clique. For other uses, see Clique disambiguation. Main article: Clique problem. Alba, Richard D. Cong, J. Day, William H. Doreian, Patrick; Woodard, Katherine L. Festinger, Leon"The analysis of sociograms using matrix algebra", Human Relations2 2 : —, doi : Graham, R. Hamzaoglu, I. Karp, Richard M. Kuhl, F. Luce, R.In graph theorya maximal independent set MIS or maximal stable set is an independent set that is not a subset of any other independent set.

In other words, there is no vertex outside the independent set that may join it because it is maximal with respect to the independent set property.

A MIS is also a dominating set in the graph, and every dominating set that is independent must be maximal independent, so MISs are also called independent dominating sets. A graph may have many MISs of widely varying sizes; [1] the largest, or possibly several equally large, MISs of a graph is called a maximum independent set.

The graphs in which all maximal independent sets have the same size are called well-covered graphs. The phrase "maximal independent set" is also used to describe maximal subsets of independent elements in mathematical structures other than graphs, and in particular in vector spaces and matroids.

The above can be restated as a vertex either belongs to the independent set or has at least one neighbor vertex that belongs to the independent set. If S is a maximal independent set in some graph, it is a maximal clique or maximal complete subgraph in the complementary graph. A maximal clique is a set of vertices that induces a complete subgraphand that is not a subset of the vertices of any larger complete subgraph.

That is, it is a set S such that every pair of vertices in S is connected by an edge and every vertex not in S is missing an edge to at least one vertex in S. A graph may have many maximal cliques, of varying sizes; finding the largest of these is the maximum clique problem. Some authors include maximality as part of the definition of a clique, and refer to maximal cliques simply as cliques.

The complement of a maximal independent set, that is, the set of vertices not belonging to the independent set, forms a minimal vertex cover. That is, the complement is a vertex covera set of vertices that includes at least one endpoint of each edge, and is minimal in the sense that none of its vertices can be removed while preserving the property that it is a cover. Minimal vertex covers have been studied in statistical mechanics in connection with the hard-sphere lattice gas model, a mathematical abstraction of fluid-solid state transitions.

Every maximal independent set is a dominating seta set of vertices such that every vertex in the graph either belongs to the set or is adjacent to the set. A set of vertices is a maximal independent set if and only if it is an independent dominating set. Certain graph families have also been characterized in terms of their maximal cliques or maximal independent sets.

Examples include the maximal-clique irreducible and hereditary maximal-clique irreducible graphs. A graph is said to be maximal-clique irreducible if every maximal clique has an edge that belongs to no other maximal clique, and hereditary maximal-clique irreducible if the same property is true for every induced subgraph.

Cographs can be characterized as graphs in which every maximal clique intersects every maximal independent set, and in which the same property is true in all induced subgraphs.

Any maximal independent set in this graph is formed by choosing one vertex from each triangle. Tighter bounds are possible if one limits the size of the maximal independent sets: the number of maximal independent sets of size k in any n -vertex graph is at most. Certain families of graphs may, however, have much more restrictive bounds on the numbers of maximal independent sets or maximal cliques.

If all n -vertex graphs in a family of graphs have O n edges, and if every subgraph of a graph in the family also belongs to the family, then each graph in the family can have at most O n maximal cliques, all of which have size O 1.

Interval graphs and chordal graphs also have at most n maximal cliques, even though they are not always sparse graphs. The number of maximal independent sets in n -vertex cycle graphs is given by the Perrin numbersand the number of maximal independent sets in n -vertex path graphs is given by the Padovan sequence.Subsets vs Proper Subsets.

Maximal and minimal elements

It is quite natural to realize the world through categorization of things into groups. The set theory was developed in the late nineteenth century, and now, it is omnipresent in mathematics. The application of set theory ranges from abstract mathematics to all subjects in the tangible physical world. Subset and Proper Subset are two terminologies often used in the Set Theory to introduce relationships between sets.

If each element in a set A is also a member of a set B, then set A is called a subset of B.

maximal subset

Any set itself is a sub set of the same set, because, obviously, any element that is in a set will also be in the same set. If a set is a proper subset of another set, it is always a subset of that set, i. But there can be subsets, which are not proper subsets of their superset. If two sets are equal, then they are subsets of one another, but not proper subset of one another.

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Comments Thank you so much that gave me much help. Leave a Reply Cancel reply.In computer sciencethe maximum sum subarray problem is the task of finding a contiguous subarray with the largest sum, within a given one-dimensional array A[ Some formulations of the problem also allow the empty subarray to be considered; by convention, the sum of all values of the empty subarray is zero. Each number in the input array A could be positive, negative, or zero. This problem can be solved using several different algorithmic techniques, including brute force, [2] divide and conquer, [3] dynamic programming, [4] and reduction to shortest paths.

The maximum subarray problem was proposed by Ulf Grenander in as a simplified model for maximum likelihood estimation of patterns in digitized images.

Grenander was looking to find a rectangular subarray with maximum sum, in a two-dimensional array of real numbers. A brute-force algorithm for the two-dimensional problem runs in O n 6 time; because this was prohibitively slow, Grenander proposed the one-dimensional problem to gain insight into its structure.

Grenander derived an algorithm that solves the one-dimensional problem in O n 2 time, [note 1] improving the brute force running time of O n 3.

When Michael Shamos heard about the problem, he overnight devised an O n log n divide-and-conquer algorithm for it. Soon after, Shamos described the one-dimensional problem and its history at a Carnegie Mellon University seminar attended by Jay Kadanewho designed within a minute an O n -time algorithm, [5] [6] [7] which is as fast as possible.

Grenander's two-dimensional generalization can be solved in O n 3 time either by using Kadane's algorithm as a subroutine, or through a divide-and-conquer approach. Maximum subarray problems arise in many fields, such as genomic sequence analysis and computer vision. Genomic sequence analysis employs maximum subarray algorithms to identify important biological segments of protein sequences. In computer visionmaximum-subarray algorithms are used on bitmap images to detect the brightest area in an image.

Thus, the problem can be solved with the following code, [4] [7] expressed here in Python :. This version of the algorithm will return 0 if the input contains no positive elements including when the input is empty. The algorithm can be modified to keep track of the starting and ending indices of the maximum subarray as well:. In Python, arrays are indexed starting from 0, and the end index is typically excluded, so that the subarray [22, 33] in the array [, 22, 33, ] would start at index 1 and end at index 3.

maximal subset

Similar problems may be posed for higher-dimensional arrays, but their solutions are more complicated; see, e. From Wikipedia, the free encyclopedia. This section needs attention from an expert in Computational biology. The specific problem is: fix inline tags. WikiProject Computational biology may be able to help recruit an expert. September Categories : Optimization algorithms and methods Dynamic programming. Hidden categories: All articles with unsourced statements Articles with unsourced statements from October Articles needing expert attention from September All articles needing expert attention Miscellaneous articles needing expert attention Articles with unsourced statements from March Articles with unsourced statements from October Articles with example Python programming language code.

Namespaces Article Talk. Views Read Edit View history. Help Learn to edit Community portal Recent changes Upload file. Download as PDF Printable version. Execution of Kadane's algorithm on the above example array. Blue : subarray with largest sum ending at i ; green : subarray with largest sum encountered so far; a lower case letter indicates an empty array; variable i is left implicit in Python code.This is an extended version of the subset sum problem.

Here we need to find the size of the maximum size subset whose sum is equal to the given sum. This is the further enhancement to the subset sum problem which not only tells whether the subset is possible but also the maximal subset using DP. To solve the subset sum problem, use the same DP approach as given in the subset sum problem.

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Please use ide. It returns -1 if there. Python3 program to implement. A Dynamic Programming solution. Returns size of maximum sized subset. The value of subset[i][j] will. If sum is 0, then answer is true. If sum is not 0 and set is empty. Fill the subset table in bottom up manner. This code is contributed by Chitranayal. Max count[i, j - 1]. WriteLine isSubsetSum setn, sum.

Find subset with maximum sum under given condition Maximum size of square such that all submatrices of that size have sum less than K Maximum subarray size, such that all subarrays of that size have sum less than k Size of the largest divisible subset in an Array Maximize count of subsets having product of smallest element and size of the subset at least X Maximum subset sum such that no two elements in set have same digit in them Maximum sum subset having equal number of positive and negative elements Find the maximum subset XOR of a given set Subset Sum Problem in O sum space Largest subset having with sum less than equal to sum of respective indices Find the smallest positive integer value that cannot be represented as sum of any subset of a given array First subarray having sum at least half the maximum sum of any subarray of size K Largest subset with maximum difference as 1.

Check out this Author's contributed articles. Load Comments. We use cookies to ensure you have the best browsing experience on our website. Returns size of maximum sized subset if there is a subset of set[] with sun equal to given sum.

It returns -1 if there is no subset with given sum.In recursion theorythe mathematical theory of computabilitya maximal set is a coinfinite recursively enumerable subset A of the natural numbers such that for every further recursively enumerable subset B of the natural numbers, either B is cofinite or B is a finite variant of A or B is not a superset of A. This gives an easy definition within the lattice of the recursively enumerable sets.

Maximal sets have many interesting properties: they are simplehypersimplehyperhypersimple and r-maximal; the latter property says that every recursive set R contains either only finitely many elements of the complement of A or almost all elements of the complement of A. There are r-maximal sets that are not maximal; some of them do even not have maximal supersets.

Myhill asked whether maximal sets exist and Friedberg constructed one. Soare showed that the maximal sets form an orbit with respect to automorphism of the recursively enumerable sets under inclusion modulo finite sets.

Maximum subarray problem

On the one hand, every automorphism maps a maximal set A to another maximal set B ; on the other hand, for every two maximal sets AB there is an automorphism of the recursively enumerable sets such that A is mapped to B. From Wikipedia, the free encyclopedia. References [ edit ] Friedberg, Richard M. Maximal set.

Rogers, Jr. Soare, Robert I.

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maximal subset

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