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Where the REALLY Hard Problems Are

by Peter Cheeseman, Bob Kanefsky, William M. Taylor - IN J. MYLOPOULOS AND R. REITER (EDS.), PROCEEDINGS OF 12TH INTERNATIONAL JOINT CONFERENCE ON AI (IJCAI-91),VOLUME 1 , 1991
"... It is well known that for many NP-complete problems, such as K-Sat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NP-complete problems can be summarized by at least one "order parameter", and that the hard p ..."
Abstract - Cited by 683 (1 self) - Add to MetaCart
It is well known that for many NP-complete problems, such as K-Sat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NP-complete problems can be summarized by at least one "order parameter", and that the hard

Proof verification and hardness of approximation problems

by Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu Sudan, Mario Szegedy - IN PROC. 33RD ANN. IEEE SYMP. ON FOUND. OF COMP. SCI , 1992
"... We show that every language in NP has a probablistic verifier that checks membership proofs for it using logarithmic number of random bits and by examining a constant number of bits in the proof. If a string is in the language, then there exists a proof such that the verifier accepts with probabilit ..."
Abstract - Cited by 797 (39 self) - Add to MetaCart
in the proof (though this number is a very slowly growing function of the input length). As a consequence we prove that no MAX SNP-hard problem has a polynomial time approximation scheme, unless NP=P. The class MAX SNP was defined by Papadimitriou and Yannakakis [82] and hard problems for this class include

A New Method for Solving Hard Satisfiability Problems

by Bart Selman, Hector Levesque, David Mitchell - 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 ..."
Abstract - Cited by 730 (21 self) - Add to MetaCart
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

Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment

by C.L. Liu, James Layland , 1973
"... The problem of multiprogram scheduling on a single processor is studied from the viewpoint... ..."
Abstract - Cited by 3756 (3 self) - Add to MetaCart
The problem of multiprogram scheduling on a single processor is studied from the viewpoint...

Pushing the Envelope: Planning, Propositional Logic, and Stochastic Search

by Henry Kautz, Bart Selman , 1996
"... Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning pr ..."
Abstract - Cited by 579 (33 self) - Add to MetaCart
Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning

A New Kind of Science

by Stephen Wolfram , 2002
"... “Somebody says, ‘You know, you people always say that space is continuous. How do you know when you get to a small enough dimension that there really are enough points in between, that it isn’t just a lot of dots separated by little distances? ’ Or they say, ‘You know those quantum mechanical amplit ..."
Abstract - Cited by 893 (0 self) - Add to MetaCart
“Somebody says, ‘You know, you people always say that space is continuous. How do you know when you get to a small enough dimension that there really are enough points in between, that it isn’t just a lot of dots separated by little distances? ’ Or they say, ‘You know those quantum mechanical

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

by Peter W. Shor - SIAM J. on Computing , 1997
"... A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. ..."
Abstract - Cited by 1277 (4 self) - Add to MetaCart
. This paper considers factoring integers and finding discrete logarithms, two problems which are generally thought to be hard on a classical computer and which have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical

Learning to predict by the methods of temporal differences

by Richard S. Sutton - MACHINE LEARNING , 1988
"... This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predi ..."
Abstract - Cited by 1521 (56 self) - Add to MetaCart
more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.

A Threshold of ln n for Approximating Set Cover

by Uriel Feige - JOURNAL OF THE ACM , 1998
"... Given a collection F of subsets of S = f1; : : : ; ng, set cover is the problem of selecting as few as possible subsets from F such that their union covers S, and max k-cover is the problem of selecting k subsets from F such that their union has maximum cardinality. Both these problems are NP-har ..."
Abstract - Cited by 776 (5 self) - Add to MetaCart
Given a collection F of subsets of S = f1; : : : ; ng, set cover is the problem of selecting as few as possible subsets from F such that their union covers S, and max k-cover is the problem of selecting k subsets from F such that their union has maximum cardinality. Both these problems are NP-hard

Metaphors We Live By

by George Lakoff, Mark Johnson , 1980
"... 1. Make a list of some of the metaphors discussed by Lakoff and Johnson. Try inserting new words that convey a different meaning. For example, consider the expression, “I’d like to share some time with you ” rather than “spend some time with you.” 2. Make a list of “language asymmetries ” (see Part ..."
Abstract - Cited by 3387 (7 self) - Add to MetaCart
the “medicalized ” terms you hear for a few days (for example, erectile dysfunction, hyperkinesis). Try substituting more common terms and see if you think about the “problem ” differently. For example, clinically depressed versus tired and really burnt out. Do these problems seem more real or authentic
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