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About 10,000 papers for “Approximation algorithm”

Approximation Algorithm

Www. Tijournals. Com, Sonam Gupta, Neeti Chadha et al. · 2015

In this paper a new approximation algorithm for calculating the min-cut tree of an undirected edge-weighted graph has been proposed. This algorithm runs in O(V2.logV + V2.d) where V is the number of vertices in the given graph and d is the degree of the graph. It is a significant improvement over time complexities of existing solutions. However, because of an assumption it does not produce correct result for all the sorts of graphs but for the dense graphs success rate is more than 90%. Moreover in the unsuccessful cases, the deviations from actual result is very less and for most of the pairs correct values of max-flow or min-cut have been obtained. This algorithm is implemented in JAVA language and checked for many input cases.

Cited by 0

Approximation Algorithm Project

Chiranjeev Kumar · arXiv (Cornell University) · 2014

This application for learning APPROXIMATION ALGORITHM has been designed in Java which will make user comfortable in learning the very complex subject "NP-Completeness" and the solution to NP-Complete problem using approximation algorithm.

Cited by 0Open access

A Simple Approximation Algorithm in. . .

M.A. Shokrollahi, V. Stemann · 1996

We describe a very simple and efficient new algorithm for the approximation of complex numbers by algebraic integers in Z[e 2i=8 ] whose coeffcients with respect to the usual basis are bounded in absolute value by a given integer M . Its main idea is the use of a novel signature technique. An important application is the reduction of dynamic range requirements for residue number system implementations of the discrete Fourier transform. The algorithm uses at most 10 log(M) arithmetic steps and 2:4 log(M) additional memroy. It yields approximations within a distance of at most 3:42=M . Several examples are included which show that the algorithm is very fast in practice. For instance, 50000 complex approximations take less than 0.7 seconds on a SPARC-5. ii 1 Introduction In a pioneering paper Cozzens and Finkelstein [1] suggest to use cyclotomic integers in the ring Z[i] := n ff 0 + ff 1 i + : : : + ff 2 n\\Gamma1 \\Gamma1 i 2 n\\Gamma1 \\Gamma1 fi fi fi ff i 2 Z o , i = e 2i=2...

Cited by 0

approximation algorithm for Minimum Degree

R. Ravi, Mohit Singh, R. Ravi et al. · 2016

Abstract. In this paper, we study the minimum degree minimum span-ning tree problem: Given a graph G = (V,E) and a non-negative cost function c on the edges, the objective is to find a minimum cost spanning tree T under the cost function c such that the maximum degree of any node in T is minimized. We obtain an algorithm which returns an MST of maximum degree at most ∆∗+k where ∆ ∗ is the minimum maximum degree of any MST and k is the distinct number of costs in any MST of G. We use a lower bound given by a linear programming relaxation to the problem and strengthen known graph-theoretic results on minimum degree subgraphs [3, 5] to prove our result. Previous results for the problem [1, 4] used a combina-torial lower bound which is weaker than the LP bound we use. 1

Cited by 0

A Scaled Stochastic Approximation Algorithm

Sigrún Andradóttir · Management Science · 1996

Consider a stochastic system of such complexity that its performance can only be evaluated by using simulation or direct experimentation. To optimize the expected performance of such systems as a function of several continuous input parameters (decision variables), we present a “scaled” stochastic approximation algorithm for finding the zero (root) of the gradient of the response function. In each iteration of the scaled algorithm, two independent gradient estimates are sampled at the current estimate of the optimal input-parameter vector to compute a scale-free estimate of the next search direction. We establish sufficient conditions to ensure strong consistency and asymptotic normality of the resulting estimator of the optimal input-parameter vector. Strong consistency is also established for a variant of the scaled algorithm with Kesten's acceleration. An experimental performance comparison of the scaled algorithm and the classical Robbins-Monro algorithm in two simple queueing systems reveals some of the practical advantages of the scaled algorithm.

Cited by 56

A projected stochastic approximation algorithm

S. Andradottir · 1991 Winter Simulation Conference Proceedings. · 2002

It is noted that classical stochastic approximation algorithms often diverge because of boundedness problems. The standard approach to preventing this is to project the sequence generated by the algorithm onto a predetermined compact set K. However, in the typical application, the approximate location of the solution is not known. To minimize the probability that the solution lies outside the set K, it is therefore necessary to let K be large. This can seriously curtail the efficiency of the algorithm. The author proposes a stochastic approximation algorithm which bounds the sequence of estimates of the solution to an increasing sequence of sets. This eliminates the possibility of bounding the algorithm to a set which does not contain the solution. Furthermore, it is possible to let the initial set be small, which can result in improved empirical performance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 10