Time Complexity Computer Science (10 results)

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! In computational complexity theory, the complexity class NP-complete (abbreviated NP-C or NPC), is a class of problems having two properties: Any given solution to the problem can be verified quickly (in polynomial time); the set of problems with this property is called NP (nondeterministic polynomial time). If the problem can be solved quickly (in polynomial time), then so can every problem in NP. Although any given solution to such a problem can be verified quickly, there is no known efficient way to locate a solution in the first place; indeed, the most notable characteristic of NP-complete problems is that no fast solution to them is known. That is, the time required to solve the problem using any currently known algorithm increases very quickly as the size of the problem grows. As a result, the time required to solve even moderately large versions of many of these problems easily reaches into the billions or trillions of years, using any amount of computing power available today. As a consequence, determining whether or not it is possible to solve these problems quickly is one of the principal unsolved problems in computer science today.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! The relationship between the complexity classes P and NP is an unsolved question in theoretical computer science. It is considered to be the most important problem in the field. In essence, the question P = NP asks: if 'yes'-answers to a 'yes'-or-'no'-question can be verified 'quickly', can the answers themselves also be computed quickly An answer to the P = NP question would determine whether problems like the subset-sum problem are as 'easy' to compute as to verify. If it turned out P does not equal NP, it would mean that some NP problems are substantially 'harder' to compute than to verify.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the size of the input to the problem. The time complexity of an algorithm is commonly expressed using the big O notation, which suppresses multiplicative constants and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e., as the input size goes to infinity. For example, if the time required by an algorithm on all inputs of size n is at most 5n3 + 3n, the asymptotic time complexity is O(n3).Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, where an elementary operation takes a fixed amount of time to perform. Thus the amount of time taken and the number of elementary operations performed by the algorithm differ by at most a constant factor.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! In computer science, polynomial time refers to the running time of an algorithm, that is, the number of computation steps a computer or an abstract machine requires to evaluate the algorithm. An algorithm is said to be polynomial time if its running time is upper bounded by a polynomial in the size of the input for the algorithm. Problems for which a polynomial time algorithm exists belong to the complexity class PTIME, which is central in the field of computational complexity theory. Cobham's thesis states that polynomial time is a synonym for 'tractable', 'feasible', 'efficient', or 'fast'.

Taschenbuch. Condition: Neu. NP-Complete | Computational Complexity Theory, Complexity Class, Polynomial Time, Unsolved Problems in Computer Science, Approximation Algorithm, Subset | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130333287 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. P Versus NP Problem | Complexity Class, Theoretical Computer Science, Decision Problem, Polynomial Time, Subset Sum Problem, Subset | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130335588 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. Time Complexity | Computer Science, Big O Notation, Binary Numeral System, Binary Tree, Parallel Random Access Machine, Frover's Algorithm, Parallel Algorithm | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130552947 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. Insertion sort | Sorting algorithm, Comparison sort, Quicksort, Heapsort, Merge sort, Adaptive sort, Time complexity, Inversion (computer science), Selection sort, Bubble sort, Stable sort, In- place algorithm | Frederic P. Miller (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130767860 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

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