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Limited Discrepancy Search

by William D. Harvey, Matthew L. Ginsberg - In Proceedings IJCAI’95 , 1995
"... Many problems of practical interest can be solved using tree search methods because carefully tuned successor ordering heuristics guide the search toward regions of the space that are likely to contain solutions. For some problems, the heuristics often lead directly to a solution— but not always. Li ..."
Abstract - Cited by 304 (5 self) - Add to MetaCart
. Limited discrepancy search addresses the problem of what to do when the heuristics fail. Our intuition is that a failing heuristic might well have succeeded if it were not for a small number of "wrong turns " along the way. For a binary tree of height d, there are only d ways the heuristic could

Improved Limited Discrepancy Search

by Richard E. Korf - In Proceedings of AAAI-96 , 1996
"... We present an improvement to Harvey and Ginsberg's limited discrepancy search algorithm. Our version eliminates much of the redundancy in the original algorithm, generating each search path from the root to the maximum search depth only once. For a uniform-depth binary tree of depth d, this red ..."
Abstract - Cited by 73 (3 self) - Add to MetaCart
We present an improvement to Harvey and Ginsberg's limited discrepancy search algorithm. Our version eliminates much of the redundancy in the original algorithm, generating each search path from the root to the maximum search depth only once. For a uniform-depth binary tree of depth d

Limited discrepancy beam search

by David Furcy - In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI , 2005
"... Beam search reduces the memory consumption of bestfirst search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memory-bounded search method th ..."
Abstract - Cited by 29 (2 self) - Add to MetaCart
Beam search reduces the memory consumption of bestfirst search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memory-bounded search method

Depth-bounded Discrepancy Search

by Toby Walsh - In Proceedings of IJCAI-97 , 1997
"... Many search trees are impractically large to explore exhaustively. Recently, techniques like limited discrepancy search have been proposed for improving the chance of finding a goal in a limited amount of search. Depth-bounded discrepancy search offers such a hope. The motivation behind depth-bounde ..."
Abstract - Cited by 88 (0 self) - Add to MetaCart
Many search trees are impractically large to explore exhaustively. Recently, techniques like limited discrepancy search have been proposed for improving the chance of finding a goal in a limited amount of search. Depth-bounded discrepancy search offers such a hope. The motivation behind depth

Equilibrium and welfare in markets with financially constrained arbitrageurs

by Denis Gromb , Dimitri Vayanos , 2002
"... We propose a multiperiod model in which competitive arbitrageurs exploit discrepancies between the prices of two identical risky assets traded in segmented markets. Arbitrageurs need to collateralize separately their positions in each asset, and this implies a financial constraint limiting positions ..."
Abstract - Cited by 272 (21 self) - Add to MetaCart
We propose a multiperiod model in which competitive arbitrageurs exploit discrepancies between the prices of two identical risky assets traded in segmented markets. Arbitrageurs need to collateralize separately their positions in each asset, and this implies a financial constraint limiting

Scaling limits for the Lego discrepancy

by André Van Hameren, Ronald Kleiss - Nucl. Phys. B 558 [PM , 1999
"... For the Lego discrepancy with M bins, which is equivalent with a χ 2-statistic with M bins, we present a procedure to calculate the moment generating function of the probability distribution perturbatively if M and N, the number of uniformly and randomly distributed data points, become large. Furthe ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
For the Lego discrepancy with M bins, which is equivalent with a χ 2-statistic with M bins, we present a procedure to calculate the moment generating function of the probability distribution perturbatively if M and N, the number of uniformly and randomly distributed data points, become large

Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems

by Paul Shaw , 1998
"... We use a local search method we term Large Neighbourhood Search (LNS) for solving vehicle routing problems. LNS meshes well with constraint programming technology and is analogous to the shuffling technique of job-shop scheduling. The technique explores a large neighbourhood of the current solution ..."
Abstract - Cited by 212 (2 self) - Add to MetaCart
by selecting a number of customer visits to remove from the routing plan, and re-inserting these visits using a constraint-based tree search. We analyse the performance of LNS on a number of vehicle routing benchmark problems. Unlike related methods, we use Limited Discrepancy Search during the tree search

Minimal discrepancies of toric singularities

by Alexandr Borisov - Manuscripta Math , 1997
"... The main purpose of this paper is to prove that minimal discrepancies of n-dimensional toric singularities can accumulate only from above and only to minimal discrepancies of toric singularities of dimension less than n. I also prove that some lower-dimensional minimal discrepancies do appear as suc ..."
Abstract - Cited by 15 (4 self) - Add to MetaCart
The main purpose of this paper is to prove that minimal discrepancies of n-dimensional toric singularities can accumulate only from above and only to minimal discrepancies of toric singularities of dimension less than n. I also prove that some lower-dimensional minimal discrepancies do appear

Gaussian limits for discrepancies. I: Asymptotic results

by Andr'e Van Hameren, Ronald Kleiss, Jiri Hoogland
"... We consider the problem of finding, for a given quadratic measure of non-uniformity of a set of N points (such as L 2 star-discrepancy or diaphony), the asymptotic distribution of this discrepancy for truly random points in the limit N ! 1. We then examine the circumstances under which this distribu ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
We consider the problem of finding, for a given quadratic measure of non-uniformity of a set of N points (such as L 2 star-discrepancy or diaphony), the asymptotic distribution of this discrepancy for truly random points in the limit N ! 1. We then examine the circumstances under which

Limited Arbitrage and Pricing Discrepancies in Credit Markets

by Kasing Man, Junbo Wang, Chunchi Wu , 2013
"... In this paper we investigate the roles of nondefault risk factors and impediments to arbitrage in the pricing discrepancies of credit markets. We find that nondefault factors contribute to the unusual price distortions in credit markets and the noise in CDS price signals during the crisis. However, ..."
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In this paper we investigate the roles of nondefault risk factors and impediments to arbitrage in the pricing discrepancies of credit markets. We find that nondefault factors contribute to the unusual price distortions in credit markets and the noise in CDS price signals during the crisis. However
Results 1 - 10 of 1,751