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101.163.28.123 at 22:56, 21 November 2014

2014-11-21T22:56:56Z

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	In [[constraint satisfaction problem\|constraint satisfaction]] [[backtracking]] [[algorithm]]s, '''constraint learning''' is a technique for improving efficiency. It works by recording new constraints whenever an inconsistency is found. This new constraint may reduce the [[Candidate solution\|search space]], as future partial evaluations may be found inconsistent without further search. '''Clause learning''' is the name of this technique when applied to [[propositional satisfiability]].		Aircraft Maintenance Engineer (Mechanical ) Alphonse Daloia from Noelville, enjoys to spend some time computers, ganhando dinheiro na internet and rowing. Recently took some time to visit The Buda Castle Quarter and Andrássy Avenue.<br><br>my blog post - [http://comoganhardinheiropelainternet.comoganhardinheiro101.com ganhe dinheiro]

	~~==Definition==~~

	Backtracking algorithms work by choosing an unassigned variable and recursively solve the problems obtained by assigning a value to this variable. Whenever the current partial solution is found inconsistent, the algorithm goes back to the previously assigned variable, ~~as expected by recursion. A constraint learning algorithm differs because it tries~~ to ~~record~~ some information, before backtracking, in form of a new constraint. This can reduce the further search because the subsequent search may encounter another partial solution that is inconsistent with this new constraint. If the algorithm has learned the new constraint, it will backtrack from this solution, while the original backtracking algorithm would do a subsequent search.

	If the partial solution <math>x_1=a_1,\ldots,x_k=a_k</math> is inconsistent, the problem instance implies the constraint stating that <math>x_i=a_i</math> cannot be true for all <math>i \in [1,k]</math> at the same time~~. However, recording this constraint is not useful, as this partial solution will not be encountered again due to the way backtracking proceeds.~~

	On the other hand, if a subset of this evaluation is inconsistent, the corresponding constraint may be useful in the subsequent search, as the same subset of the partial evaluation may occur again in the search. For example, the algorithm may encounter an evaluation extending the subset <math>x_2=a_2, ~~x_5=a_5, x_{k-1}=a_{k-1}</math> of the previous partial evaluation. If this subset is inconsistent~~ and ~~the algorithm has stored this fact in form of a constraint, no further search is needed to conclude that the new partial evaluation cannot be extended to form a solution~~.

	~~{\| cellpadding=20~~
	\|-
	~~\| [[Image:Constraint-learning-1.svg]]~~
	~~\| [[Image:Constraint-learning-2.svg]]~~
	~~\| [[Image:Constraint-learning-3.svg]]~~
	\|-
	~~\| Search has reached a dead end.~~
	~~\| Inconsistency may be caused by the values of <math>x_1</math> and <math>x_4</math> only. This fact can be stored in a new constraint.~~
	~~\| If the algorithm reaches the same values of <math>x_1</math> and <math>x_4</math> again, the new constraint blocks the search.~~
	\|}

	~~==Efficiency of constraint learning==~~

	The efficiency of constraint learning algorithm is balanced between two factors. On one hand, the more often a recorded constraint is violated, the more often backtracking avoids doing useless search. Small inconsistent subsets of the current partial solution are usually better than large ones, as they correspond to constraints that are easier to violate. On the other hand, finding a small inconsistent subset of the current partial evaluation may require time~~, and the benefit may not be balanced by the subsequent reduction of the search time.~~

	~~Size is however not the only feature of learned constraints~~ to take into account. Indeed, a small constraint may be useless in a particular state of the search space because the values that violate it will not be encountered again. A larger constraint whose violating values are more similar to the current partial assignment may be preferred in such cases.

	~~Various constraint learning techniques exist, differing in strictness of recorded constraints~~ and ~~cost of finding them.~~

	~~==Graph-based learning==~~

	If the algorithm proves all values of <math>x_{k+1}</math> to be inconsistent with <math>x_1=a_1,\ldots,x_k=a_k</math>, then this evaluation was consistent, as otherwise the algorithm would not have evaluated <math>x_{k+1}</math> at all; as a result, the constraints violated by a value of <math>x_{k+1}</math> together with <math>x_1=a_1,\ldots,x_k=a_k</math> all contain <math>x_{k+1}</math>.

	~~As a result, an inconsistent evaluation is the restriction of the truth evaluation of <math>x_1,\ldots,x_k</math> to variables that are in a constraint with~~ <~~math~~>~~x_{k+1}~~<~~/math~~>~~, provided that this constraint contains no unassigned variable.~~

	Learning constraints representing these partial evaluation is called graph-based learning. It uses the same rationale of [[graph-based backjumping]]. These methods are called "graph-based" because they are based on pairs of variables are in the same constraint, which can be found out from the graph associated to the constraint satisfaction problem.

	~~==Jumpback learning==~~

	Jumpback learning is based on storing as constraints the inconsistent assignments that would be found by [[conflict-based backjumping]]. Whenever a partial assignment is found inconsistent, this algorithm selects the violated constraint that is minimal according to an ordering based on the order of instantiation of variables. The evaluation restricted of the variables that are in this constraint is inconsistent and is usually shorter than the complete evaluation. Jumpback learning stores this fact as a new constraint.

	The ordering on constraints is based on the order of assignment of variable. In particular, the least of two constraint is the one whose latest non-common variable has been instantiated first. When an inconsistent assignment is reached, jumpback learning selects the violated constraint that is minimal according to this ordering, and restricts the current assignment to its variables. The constraint expressing the inconsistency of this assignment is stored.

	~~==Constraint maintenance==~~

	Constraint learning algorithms differ not only on the choice of constraint corresponding to a given inconsistent partial evaluation, but also on the choice of which constraint they maintain and which ones they discard.

	In general, learning all inconsistencies in form of constraints and keeping them indefinitedly may exhaust the available memory and increase the cost of checking consistency of partial evaluations. These problems can be solved either by storing only some learned constraints or by occasionally discarding constraints.

	~~''Bounded learning'' only stores constraints if the inconsistent partial evaluation they represent is smaller than a given constrant number. ''Relevance~~-bounded learning'' discards constraints (or does not store them at all) that are considered not relevant given the current point of the search space; in particular, it discards or does not store all constraints that represent inconsistent partial evaluations that differ from the current partial evaluation on no more than a given fixed number of variables.

	~~==See also==~~

	*[~~[Look-ahead (backtracking)]]~~
	*[[Backjumping]]

	~~==References==~~

	*{{cite book
	~~\| first=Rina~~
	~~\| last=Dechter~~
	~~\| title=Constraint Processing~~
	~~\| publisher=Morgan Kaufmann~~
	~~\| year=2003~~
	~~\| url=~~http://~~www.ics.uci~~.~~edu/~dechter/books/index~~.~~html~~
	~~}} ISBN 1-55860-890-7~~

	~~[[Category:Constraint programming]~~]

18.189.42.27: minor typo

2013-07-17T19:48:03Z

minor typo

← Older revision		Revision as of 21:48, 17 July 2013
Line 1:		Line 1:
	~~Hi there~~. ~~My title~~ is ~~Sophia Meagher even though~~ it is not the ~~name on my beginning certification~~. ~~To perform lacross~~ is 1 of the ~~things she loves most~~. ~~Her family life~~ in ~~Ohio but her husband wants~~ them to ~~move~~. ~~Distributing manufacturing~~ is ~~where her primary income arrives from.~~<br><br>~~Feel free~~ to ~~visit my web blog~~ ... ~~clairvoyants~~ ([http://www.~~youronlinepublishers~~.~~com~~/~~authWiki~~/~~AdolphvhBladenqq hop over to this web~~-~~site~~])		In [[constraint satisfaction problem\|constraint satisfaction]] [[backtracking]] [[algorithm]]s, '''constraint learning''' is a technique for improving efficiency. It works by recording new constraints whenever an inconsistency is found. This new constraint may reduce the [[Candidate solution\|search space]], as future partial evaluations may be found inconsistent without further search. '''Clause learning''' is the name of this technique when applied to [[propositional satisfiability]].

			==Definition==

			Backtracking algorithms work by choosing an unassigned variable and recursively solve the problems obtained by assigning a value to this variable. Whenever the current partial solution is found inconsistent, the algorithm goes back to the previously assigned variable, as expected by recursion. A constraint learning algorithm differs because it tries to record some information, before backtracking, in form of a new constraint. This can reduce the further search because the subsequent search may encounter another partial solution that is inconsistent with this new constraint. If the algorithm has learned the new constraint, it will backtrack from this solution, while the original backtracking algorithm would do a subsequent search.

			If the partial solution <math>x_1=a_1,\ldots,x_k=a_k</math> is inconsistent, the problem instance implies the constraint stating that <math>x_i=a_i</math> cannot be true for all <math>i \in [1,k]</math> at the same time. However, recording this constraint is not useful, as this partial solution will not be encountered again due to the way backtracking proceeds.

			On the other hand, if a subset of this evaluation is inconsistent, the corresponding constraint may be useful in the subsequent search, as the same subset of the partial evaluation may occur again in the search. For example, the algorithm may encounter an evaluation extending the subset <math>x_2=a_2, x_5=a_5, x_{k-1}=a_{k-1}</math> of the previous partial evaluation. If this subset is inconsistent and the algorithm has stored this fact in form of a constraint, no further search is needed to conclude that the new partial evaluation cannot be extended to form a solution.

			{\| cellpadding=20
			\|-
			\| [[Image:Constraint-learning-1.svg]]
			\| [[Image:Constraint-learning-2.svg]]
			\| [[Image:Constraint-learning-3.svg]]
			\|-
			\| Search has reached a dead end.
			\| Inconsistency may be caused by the values of <math>x_1</math> and <math>x_4</math> only. This fact can be stored in a new constraint.
			\| If the algorithm reaches the same values of <math>x_1</math> and <math>x_4</math> again, the new constraint blocks the search.
			\|}

			==Efficiency of constraint learning==

			The efficiency of constraint learning algorithm is balanced between two factors. On one hand, the more often a recorded constraint is violated, the more often backtracking avoids doing useless search. Small inconsistent subsets of the current partial solution are usually better than large ones, as they correspond to constraints that are easier to violate. On the other hand, finding a small inconsistent subset of the current partial evaluation may require time, and the benefit may not be balanced by the subsequent reduction of the search time.

			Size is however not the only feature of learned constraints to take into account. Indeed, a small constraint may be useless in a particular state of the search space because the values that violate it will not be encountered again. A larger constraint whose violating values are more similar to the current partial assignment may be preferred in such cases.

			Various constraint learning techniques exist, differing in strictness of recorded constraints and cost of finding them.

			==Graph-based learning==

			If the algorithm proves all values of <math>x_{k+1}</math> to be inconsistent with <math>x_1=a_1,\ldots,x_k=a_k</math>, then this evaluation was consistent, as otherwise the algorithm would not have evaluated <math>x_{k+1}</math> at all; as a result, the constraints violated by a value of <math>x_{k+1}</math> together with <math>x_1=a_1,\ldots,x_k=a_k</math> all contain <math>x_{k+1}</math>.

			As a result, an inconsistent evaluation is the restriction of the truth evaluation of <math>x_1,\ldots,x_k</math> to variables that are in a constraint with <math>x_{k+1}</math>, provided that this constraint contains no unassigned variable.

			Learning constraints representing these partial evaluation is called graph-based learning. It uses the same rationale of [[graph-based backjumping]]. These methods are called "graph-based" because they are based on pairs of variables are in the same constraint, which can be found out from the graph associated to the constraint satisfaction problem.

			==Jumpback learning==

			Jumpback learning is based on storing as constraints the inconsistent assignments that would be found by [[conflict-based backjumping]]. Whenever a partial assignment is found inconsistent, this algorithm selects the violated constraint that is minimal according to an ordering based on the order of instantiation of variables. The evaluation restricted of the variables that are in this constraint is inconsistent and is usually shorter than the complete evaluation. Jumpback learning stores this fact as a new constraint.

			The ordering on constraints is based on the order of assignment of variable. In particular, the least of two constraint is the one whose latest non-common variable has been instantiated first. When an inconsistent assignment is reached, jumpback learning selects the violated constraint that is minimal according to this ordering, and restricts the current assignment to its variables. The constraint expressing the inconsistency of this assignment is stored.

			==Constraint maintenance==

			Constraint learning algorithms differ not only on the choice of constraint corresponding to a given inconsistent partial evaluation, but also on the choice of which constraint they maintain and which ones they discard.

			In general, learning all inconsistencies in form of constraints and keeping them indefinitedly may exhaust the available memory and increase the cost of checking consistency of partial evaluations. These problems can be solved either by storing only some learned constraints or by occasionally discarding constraints.

			''Bounded learning'' only stores constraints if the inconsistent partial evaluation they represent is smaller than a given constrant number. ''Relevance-bounded learning'' discards constraints (or does not store them at all) that are considered not relevant given the current point of the search space; in particular, it discards or does not store all constraints that represent inconsistent partial evaluations that differ from the current partial evaluation on no more than a given fixed number of variables.

			==See also==

			*[[Look-ahead (backtracking)]]
			*[[Backjumping]]

			==References==

			*{{cite book
			\| first=Rina
			\| last=Dechter
			\| title=Constraint Processing
			\| publisher=Morgan Kaufmann
			\| year=2003
			\| url=http://www.ics.uci.edu/~dechter/books/index.html
			}} ISBN 1-55860-890-7

			[[Category:Constraint programming]]

en>Mauromaiorca at 12:13, 19 May 2012

2012-05-19T12:13:31Z

New page

Hi there. My title is Sophia Meagher even though it is not the name on my beginning certification. To perform lacross is 1 of the things she loves most. Her family life in Ohio but her husband wants them to move. Distributing manufacturing is where her primary income arrives from.<br><br>Feel free to visit my web blog ... clairvoyants ([http://www.youronlinepublishers.com/authWiki/AdolphvhBladenqq hop over to this web-site])