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In logic and computer science, the Boolean satisfiability problem (sometimes called propositional satisfiability problem and abbreviated SATISFIABILITY, SAT or B-SAT) is the problem of determining if there exists an interpretation that satisfies a given Boolean formula. In other words, it asks whether the variables of a given Boolean formula can be consistently replaced by the values TRUE or FALSE in such a way that the formula evaluates to TRUE. If this is the case, the formula is called satisfiable. On the other hand, if no such assignment exists, the function expressed by the formula is FALSE for all possible variable assignments and the formula is unsatisfiable. For example, the formula "a AND NOT b" is satisfiable because one can find the values a = TRUE and b = FALSE, which make (a AND NOT b) = TRUE. In contrast, "a AND NOT a" is unsatisfiable.
The P versus NP problem is a major unsolved problem in theoretical computer science. Informally, it asks whether every problem whose solution can be quickly verified can also be quickly solved.
In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and explores the relationships between these classifications. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm.
The #P-complete problems form a complexity class in computational complexity theory. The problems in this complexity class are defined by having the following two properties:
In computational complexity theory, a polynomial-time reduction is a method for solving one problem using another. One shows that if a hypothetical subroutine solving the second problem exists, then the first problem can be solved by transforming or reducing it to inputs for the second problem and calling the subroutine one or more times. If both the time required to transform the first problem to the second, and the number of times the subroutine is called is polynomial, then the first problem is polynomial-time reducible to the second.
In graph theory, an isomorphism of graphsG and H is a bijection between the vertex sets of G and H
In computer science, the clique problem is the computational problem of finding cliques in a graph. It has several different formulations depending on which cliques, and what information about the cliques, should be found. Common formulations of the clique problem include finding a maximum clique, finding a maximum weight clique in a weighted graph, listing all maximal cliques, and solving the decision problem of testing whether a graph contains a clique larger than a given size.
The graph isomorphism problem is the computational problem of determining whether two finite graphs are isomorphic.
In computational complexity theory, a language B is said to be low for a complexity class A if AB = A; that is, A with an oracle for B is equal to A. Such a statement implies that an abstract machine which solves problems in A achieves no additional power if it is given the ability to solve problems in B at unit cost. In particular, this means that if B is low for A then B is contained in A. Informally, lowness means that problems in B are not only solvable by machines which can solve problems in A, but are “easy to solve”. An A machine can simulate many oracle queries to B without exceeding its resource bounds.
In computational complexity theory, the complexity class ⊕P is the class of decision problems solvable by a nondeterministic Turing machine in polynomial time, where the acceptance condition is that the number of accepting computation paths is odd. An example of a ⊕P problem is "does a given graph have an odd number of perfect matchings?" The class was defined by Papadimitriou and Zachos in 1983.
László "Laci" Babai is a Hungarian professor of computer science and mathematics at the University of Chicago. His research focuses on computational complexity theory, algorithms, combinatorics, and finite groups, with an emphasis on the interactions between these fields.
In computational complexity, problems that are in the complexity class NP but are neither in the class P nor NP-complete are called NP-intermediate, and the class of such problems is called NPI. Ladner's theorem, shown in 1975 by Richard E. Ladner, is a result asserting that, if P ≠ NP, then NPI is not empty; that is, NP contains problems that are neither in P nor NP-complete. Since it is also true that if NPI problems exist, then P ≠ NP, it follows that P = NP if and only if NPI is empty.
In the mathematical field of graph theory, an automorphism of a graph is a form of symmetry in which the graph is mapped onto itself while preserving the edge–vertex connectivity.
In graph theory, a branch of mathematics, graph canonization is the problem of finding a canonical form of a given graph G. A canonical form is a labeled graph Canon(G) that is isomorphic to G, such that every graph that is isomorphic to G has the same canonical form as G. Thus, from a solution to the graph canonization problem, one could also solve the problem of graph isomorphism: to test whether two graphs G and H are isomorphic, compute their canonical forms Canon(G) and Canon(H), and test whether these two canonical forms are identical.
In computational complexity theory, a problem is NP-complete when:
- It is a decision problem, meaning that for any input to the problem, the output is either "yes" or "no".
- When the answer is "yes", this can be demonstrated through the existence of a short solution.
- The correctness of each solution can be verified quickly and a brute-force search algorithm can find a solution by trying all possible solutions.
- The problem can be used to simulate every other problem for which we can verify quickly that a solution is correct. In this sense, NP-complete problems are the hardest of the problems to which solutions can be verified quickly. If we could find solutions of some NP-complete problem quickly, we could quickly find the solutions of every other problem to which a given solution can be easily verified.
In computational complexity theory, the exponential time hypothesis is an unproven computational hardness assumption that was formulated by Impagliazzo & Paturi (1999). It states that satisfiability of 3-CNF Boolean formulas cannot be solved in subexponential time, . More precisely, the usual form of the hypothesis asserts the existence of a number such that all algorithms that correctly solve this problem require time at least The exponential time hypothesis, if true, would imply that P ≠ NP, but it is a stronger statement. It implies that many computational problems are equivalent in complexity, in the sense that if one of them has a subexponential time algorithm then they all do, and that many known algorithms for these problems have optimal or near-optimal time complexity.
LOOP is a simple register language that precisely captures the primitive recursive functions. The language is derived from the counter-machine model. Like the counter machines the LOOP language comprises a set of one or more unbounded registers, each of which can hold a single non-negative integer. A few arithmetic instructions operate on the registers. The only control flow instruction is 'LOOP x DO...END'. It causes the instructions within its scope to be repeated x times.
Stefan Szeider is an Austrian computer scientist who works on the areas of algorithms, computational complexity, theoretical computer science, and more specifically on propositional satisfiability, constraint satisfaction problems, and parameterised complexity. He is a full professor at the Faculty of Informatics at the Vienna University of Technology, the head of the Algorithms and Complexity Group, and co-chair of the Vienna Center for Logic and Algorithms (VCLA) of TU Wien.
Uri Zwick is an Israeli computer scientist and mathematician known for his work on graph algorithms, in particular on distances in graphs and on the color-coding technique for subgraph isomorphism. With Howard Karloff, he is the namesake of the Karloff–Zwick algorithm for approximating the MAX-3SAT problem of Boolean satisfiability. He and his coauthors won the David P. Robbins Prize in 2011 for their work on the block-stacking problem.
References
- ↑ Uwe Schöning at the Mathematics Genealogy Project
- ↑ Faculty profile, Univ. of Ulm, retrieved 2022-03-28.
- ↑ Review of Complexity and Structure by Juris Hartmanis (1987), MR 0827009
- ↑ Review of Logik für Informatiker by Neculai Curteanu (1989), MR 0944086; also listed as MR 1244106 (3rd ed.) and MR 2683474 (English ed.)
- ↑ Review of Logic for Computer Scientists by Riccardo Pucella (2005), SIGACT News 36 (3): 17–19, doi : 10.1145/1086649.1086657
- ↑ Review of The Graph Isomorphism Problem by Pavol Hell (1995), MR 1232421
- ↑ Review of Gems of Theoretical Computer Science by Rohit Jivanlal Parikh (2000), Journal of Logic, Language and Information 9 (1): 131–132, doi : 10.1023/A:1008379701961
- ↑ Review of Gems of Theoretical Computer Science by Danny Krizanc (2000), SIGACT News 31 (2): 2–5, doi : 10.1145/348210.1042072
- ↑ Review of Gems of Theoretical Computer Science by Marius Zimand (2000), MR 1667079
