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Solving Large Double Digestion Problems for DNA Restriction Mapping by Using BranchandBound Integer Linear Programming
"... Abstract The double digestion problem for DNA restriction mapping has been proved to be NPcomplete, and is intractable if the numbers of the DNA fragments generated by the two restriction enzymes are large. Several approaches to the problem have been used, including exhaustive search and simulated a ..."
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formulate the double digestion problem as a mixedinteger linear program by following Waterman 1995 in a sightly different form. We show that with this formulation and by using some stateoftheart integer programming techniques, we can actually solve double digestion problems for fairly large sizes
Restriction Mapping by Using BranchandBound Integer Linear Programming
, 2007
"... Abstract. The double digestion problem for DNA restriction mapping has been proved to be NPcomplete, and is intractable if the numbers of the DNA fragments generated by the two restriction enzymes are large. Several approaches to the problem have been used, including exhaustive search and simulated ..."
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Abstract. The double digestion problem for DNA restriction mapping has been proved to be NPcomplete, and is intractable if the numbers of the DNA fragments generated by the two restriction enzymes are large. Several approaches to the problem have been used, including exhaustive search
A New Method for Solving Hard Satisfiability Problems
 AAAI
, 1992
"... We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approac ..."
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Cited by 734 (21 self)
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We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional
GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems
 SIAM J. SCI. STAT. COMPUT
, 1986
"... We present an iterative method for solving linear systems, which has the property ofminimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from the Arnoldi process for constructing an l2orthogonal basis of Krylov subspaces. It can be considered a ..."
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Cited by 2046 (40 self)
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We present an iterative method for solving linear systems, which has the property ofminimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from the Arnoldi process for constructing an l2orthogonal basis of Krylov subspaces. It can be considered
Genetic Programming
, 1997
"... Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1051 (12 self)
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Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring
Solving the Double Digestion Problem as a MixedInteger Linear Program
, 2001
"... The double digestion problem for DNA restriction mapping is known to be NPcomplete. Several approaches to the problem have been used including exhaustive search, simulated annealing, branchandbound. In this paper, we consider a mixedinteger linear programming formulation of the problem and show ..."
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Cited by 1 (1 self)
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The double digestion problem for DNA restriction mapping is known to be NPcomplete. Several approaches to the problem have been used including exhaustive search, simulated annealing, branchandbound. In this paper, we consider a mixedinteger linear programming formulation of the problem and show
Large Margin Classification Using the Perceptron Algorithm
 Machine Learning
, 1998
"... We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leaveoneout method. Like Vapnik 's maximalmargin classifier, our algorithm takes advantage of data that are linearly separable with large ..."
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Cited by 518 (2 self)
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with large margins. Compared to Vapnik's algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
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Cited by 1231 (13 self)
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We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
Branchandprice: Column generation for solving huge integer programs
 Oper. Res
, 1998
"... We discuss formulations of integer programs with a huge number of variables and their solution by column generation methods, i.e., implicit pricing of nonbasic variables to generate new columns or to prove LP optimality at a node of the branchandbound tree. We present classes of models for which th ..."
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Cited by 348 (13 self)
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We discuss formulations of integer programs with a huge number of variables and their solution by column generation methods, i.e., implicit pricing of nonbasic variables to generate new columns or to prove LP optimality at a node of the branchandbound tree. We present classes of models for which
Results 1  10
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