CSP: Algorithms and Dichotomy Conjecture Andrei A. Bulatov Simon Fraser University
Constraint Satisfaction Problem I CSP(  ) Definition: Instance:   ( V ; A ; C )  where    V  is a finite set of variables    A  is a set of values    C   is a set of constraints  Question:   whether there is  h :  V     A   such that, for any  i , is true where each  belongs to  
Constraint Satisfaction Problem II u - v - w - x - y - Q ( u,v,w ) R ( w,x ) R ( x,y ) S ( y,u )
3-COL  =   CSP(  ) Examples:  3-COL u v w x
Examples:  Linear Equations, SAT Linear Equations :   3-SAT = CSP(  ) :
Invariants and Polymorphisms Pol(  ) denotes the set of all polymorphisms of relations from   Definition  A relation  R  is  invariant  with respect to an   n - ary  operation  f   (or  f   is a  polymorphism  of  R ) if, for any  tuples  the tuple obtained  by applying  f   coordinate-wise is a member of  R
Affine relations:  Relations that can be represented by a system of linear equations Let also  ( affine  operation) Polymorphisms: Affine Relations If  are solutions then
2-clauses give rise to binary relations Let  ( median  operation)  Operation  h   is a polymorphism of  Polymorphisms: 2-SAT
   does not have any polymorphisms except for very trivial ones, e.g.  f ( x,y,z ) =y Polymorphisms: 3-COL
Polymorphisms and Complexity Theorem ( Jeavons; 1998 )  If     ,     are constraint languages such that  Pol(   )    Pol(   ), then  CSP (   ) is  log space reducible to  CSP (   ) 1 2 2 1 2 1 Larose, Tesson, 2007: This reduction can be made
A  semilattice  operation is a binary operation     satisfying the equations:  x      x  =  x ,  x      y  =  y      x ,  x     ( y      z ) = ( x      y )     z A semilattice operation induces a partial order: a      b      a      b  =  b Good Polymorphisms: Semilattice There is always a unique maximal element max( x,y ) gcd( x,y )  0 1 1 2 0 2 1 3 6 4 5
Good Polymorphisms: Semilattice u - v - w - x - y -
Good Polymorphisms: Semilattice Propagation u - v - w - x - y -
A  majority  operation is a ternary operation  h  that satisfies the equations  h ( x,x,y )  = h ( x,y,x )  = h ( y,x,x )  = x Good Polymorphisms: Majority Chinese Remainder Theorem for Majority  Let  R   be a ( k -ary) relation invariant under a majority operation, and  is some tuple.  Then if for any  i,j     {1,..., k }   there is a tuple  such that  then
Good Polymorphisms: Majority Propagation again: 2-consistency Any 2-consisted  instance has a solution u - v - w - x - y -
An  affine  operation is a ternary operation  m   that is given by  x – y + z   where  +, –   are operations of a certain Abelian group CSP  over a language invariant under an affine operation is just solving systems of linear equations Gaussian Elimination Good Polymorphisms: Affine
CSP(  )  is Boolean if     is over  {0,1} Complexity:  Boolean  CSP Theorem  (Schaefer 1978)   For a constraint language     over  {0,1}  the problem  CSP(  )  is solvable in poly time iff     has a semilattice, majority, or affine polymorphism; otherwise it is NP-complete Fine Print:   `Trivial’ languages are excluded from the theorem. These are so-called 0- or 1-valid languages, in which every instance has a solution
If     consists of a single binary relation (thought of being the edge relation of some  (di)graph  H), then CSP(  )  is also called  H-Coloring Complexity:  Graphs Theorem  (Hell, Nesetril  1990)   For a graph  H  the H-Coloring problem  is solvable in poly time iff  E(H)  has a majority polymorphism; otherwise it is NP-complete Fine Print:   Graphs here must be cores.  Then a core has a majority polymorphism iff it is a loop or an interval
5 types of local structure. Defined by the presence of polymorphisms that locally act as on of the 3 good polymorphisms unary  none affine  only affine boolean  all three lattice  majority, semilattice semilattice  semilattice Types Fine Print :   One needs to be quite creative to relate this definition to the actual definition as it was introduced in universal algebra 25 years ago.  It is good enough for our purpose, though
omits a type if it does not exhibit local structure of this type, otherwise it admits it (Dichotomy Conjecture)  CSP(  )  is solvable in polynomial time iff     omits the unary type; NP-complete otherwise CSP(  )  is in NL iff     omits the unary, affine, and semilattice types CSP(  )  is in L iff     omits the unary, affine, lattice and semilattice types;  NL-complete otherwise CSP(  )  is in  Mod  L  iff     omits the unary, lattice, and semilattice types Conjectures  p
B., Jeavons, Krokhin 2006:  if    admits the unary type,  CSP(  )  is NP-complete Jeavons et al. mid 90s:  algorithms if    has one of the 3 polymorphisms Barto, Kozik 2010; B.  2010:  propagation works iff     omits the unary and affine type Idziak, et al. 2010:  the Generalized Gaussian elimination algorithm if     omits the unary and semilattice types  (+ some extra conditions) ongoing, many people:  languages that admit the semilattice and affine types Algorithms
Schaefer 1978:     over a 2-element set Hell, Nesetril  1990:    = { E },  where  E  is binary symmetric 2006:     over a 3-element set Markovic, McKenzie  >2011:     over a 4-element case  1 case out of  left  (as of last Wednesday) B.  2003:     conservative, that is, it contains all unary relations Dichotomy results
Polymorphisms of conservative languages If  is a polymorphism of a conservative language   , then for any We look at how polymorphisms behave on 2-element subsets If for some 2-elemen subset  B   there is no polymorphism that is good on  B  then CSP(  )  is NP-complete Theorem  (B. 2003)   CSP(  )  for a conservative     on  A   is poly time iff for any 2-element  B    A   there is  f     Pol(  ) which is affine, majority, or semilattice; otherwise  CSP(  )  is NP-complete.
Edge coloured graphs G (  ) : Since semilattice operation induces an order,  red edges are directed semilattice operation majority operation affine operation
Let     be a conservative language over set  A B    A   is called an  as-component   (affine-semilattice) if it is minimal with respect to the property:  there is no affine or semilattice (directed) edge in  G (  )  sticking out of  B AS-components The remaining edges  are  majority
Let  R   be a  k -ary relation on  A ,  let  be  as-components  Positions  i   and  j   are  - related   if for any  iff I     { 1,...,k }   is a  coherent   set   w.r.t.  as-components  if  any  i,j    I   are  -related Coherent Sets
CRT for AS-Components Chinese Remainder Theorem for AS-Component Let  R         for a conservative     on  A   and  as-components such that for any  i,j    { 1,...,k }   there is a tuple  such that  Then there is such that  for all  i,j    { 1,...,k }.
Rectangularity Rectangularity Lemma Let  R         and  as-components such that  Let also  be the partition of  { 1,...,k }   into coherent sets w.r.t.  and  Then
The Algorithm On input  ( V,D , C ) run 2-consistency algorithm find as-components  such that  for any  v,w    V   as-components  are consistent find the coherent sets for each coherent set  W   solve the problem restricted to  W  and  if all such problem has a solution, any combination of such solutions gives a solution to the problem otherwise remove elements the failed as-components and start over u - v - w - x - y -
Ingredients: the graph Chinese Remainder Theorem rectangularity solving smaller problems failed components removal General Case I
The graph Cannot get a complete graph, but connected is possible Semilattice and majority edges are defined in almost the same way:  a,b   if there is a polymorphism which is semilattice  or majority on  { a,b } Affine edges:  Instead of pairs of elements use  subset  B      A   and a partition  of  B   such that there is a polymorphism that acts as an affine operation on the set  General Case II A B
Chinese Remainder Theorem :  holds Rectangularity :  does not hold,  need a weaker condition Solving smaller problems General Case III Theorem  There is a poly time algorithm such that on  ( V,A , C ) -  if for each  v    V   and any element  a   from an as-component there is a solution     with   ( v )  = a ,  then the algorithm finds a solution; -  otherwise it identifies which elements from as-components are not a part of a solution.
Failed components removal General Case IV Have to check every element if it is a part of a solution, not only maximal ones u - v - w - x - y -
Current Score: 3 : 2 Thank you! Conclusion

More Related Content

PPT
Csr2011 june17 14_00_bulatov
PDF
06. string matching
PPT
Hardness of approximation
PDF
Modal Logic
PDF
Non Standard Logics & Modal Logics
PPT
String kmp
PDF
Formal language & automata theory
PDF
27 NP Completness
Csr2011 june17 14_00_bulatov
06. string matching
Hardness of approximation
Modal Logic
Non Standard Logics & Modal Logics
String kmp
Formal language & automata theory
27 NP Completness

What's hot (20)

PDF
Du Calcul des prédicats vers Prolog
PDF
Predicate Calculus
PPTX
Hot topics
PDF
Common Fixed Point Theorem for Weakly Compatible Maps in Intuitionistic Fuzzy...
PDF
Automata
PDF
Gödel’s incompleteness theorems
PDF
New version
PDF
P versus NP
PPT
Godels First Incompleteness Theorem
PDF
RuleML2015: Binary Frontier-guarded ASP with Function Symbols
PDF
13 propositional calculus
PPTX
Computing with matrix groups, or "how dense is dense"
PDF
Truth as a logical connective
PPT
POST’s CORRESPONDENCE PROBLEM
PDF
GROUPOIDS, LOCAL SYSTEMS AND DIFFERENTIAL EQUATIONS
PPTX
np complete
PPTX
PDF
Lesson 5: Continuity
PDF
About the 2-Banach Spaces
PPT
lecture 28
Du Calcul des prédicats vers Prolog
Predicate Calculus
Hot topics
Common Fixed Point Theorem for Weakly Compatible Maps in Intuitionistic Fuzzy...
Automata
Gödel’s incompleteness theorems
New version
P versus NP
Godels First Incompleteness Theorem
RuleML2015: Binary Frontier-guarded ASP with Function Symbols
13 propositional calculus
Computing with matrix groups, or "how dense is dense"
Truth as a logical connective
POST’s CORRESPONDENCE PROBLEM
GROUPOIDS, LOCAL SYSTEMS AND DIFFERENTIAL EQUATIONS
np complete
Lesson 5: Continuity
About the 2-Banach Spaces
lecture 28
Ad

Viewers also liked (9)

PDF
PPT
LTR: Open Source Public Workstations
PDF
Cyia Next Levels Rev 11 07
PDF
Vademecum innovation
PPT
Exemples Gràfics de Funcions
PPT
Call Back
PDF
Csr2011 june15 09_30_shen
PPTX
SIUE Cougar Athletics Branding Presentation 5 20-10
PDF
Take Back Your Education (Ignite Portland 2013 Talk)
LTR: Open Source Public Workstations
Cyia Next Levels Rev 11 07
Vademecum innovation
Exemples Gràfics de Funcions
Call Back
Csr2011 june15 09_30_shen
SIUE Cougar Athletics Branding Presentation 5 20-10
Take Back Your Education (Ignite Portland 2013 Talk)
Ad

Similar to Csr2011 june17 14_00_bulatov (20)

PDF
Solvers and Applications with CP
PPT
presentation related to artificial intelligence.ppt
PPT
presentation on artificial intelligence autosaved
PPT
ConstraintSatisfaction.ppt
DOCX
Ca notes
PPT
Lesson 1 &2 -Set Theory and Functions.ppt
PDF
Rough set on concept lattice
PDF
Algebraic data types: Semilattices
PDF
Formal methods 4 - Z notation
PPTX
Ads unit 3 ppt
PDF
Lect 31_32 NP and Intractability_Part 1.pdf
PDF
Combinatorial Problems2
PPTX
Algorithm_NP-Completeness Proof
PPTX
Discrete Math Chapter 2: Basic Structures: Sets, Functions, Sequences, Sums, ...
PPT
computer notes - Data Structures - 28
PPT
Np cooks theorem
PDF
Daa chapter10
PPTX
Introduction to Posets, Hasse Diagrams, Lattices.pptx
PPTX
Algorithm Homework Help
PPTX
Constraint Satisfaction Problems_ AI2025
Solvers and Applications with CP
presentation related to artificial intelligence.ppt
presentation on artificial intelligence autosaved
ConstraintSatisfaction.ppt
Ca notes
Lesson 1 &2 -Set Theory and Functions.ppt
Rough set on concept lattice
Algebraic data types: Semilattices
Formal methods 4 - Z notation
Ads unit 3 ppt
Lect 31_32 NP and Intractability_Part 1.pdf
Combinatorial Problems2
Algorithm_NP-Completeness Proof
Discrete Math Chapter 2: Basic Structures: Sets, Functions, Sequences, Sums, ...
computer notes - Data Structures - 28
Np cooks theorem
Daa chapter10
Introduction to Posets, Hasse Diagrams, Lattices.pptx
Algorithm Homework Help
Constraint Satisfaction Problems_ AI2025

More from CSR2011 (20)

PDF
Csr2011 june14 09_30_grigoriev
PDF
Csr2011 june18 15_15_bomhoff
PDF
Csr2011 june18 15_15_bomhoff
PPTX
Csr2011 june18 14_00_sudan
PDF
Csr2011 june18 15_45_avron
PPTX
Csr2011 june18 09_30_shpilka
PDF
Csr2011 june18 12_00_nguyen
PDF
Csr2011 june18 11_00_tiskin
PDF
Csr2011 june18 11_30_remila
PDF
Csr2011 june17 17_00_likhomanov
PDF
Csr2011 june17 16_30_blin
PPTX
Csr2011 june17 09_30_yekhanin
PPTX
Csr2011 june17 09_30_yekhanin
PDF
Csr2011 june17 12_00_morin
PDF
Csr2011 june17 11_30_vyalyi
PDF
Csr2011 june17 11_00_lonati
PPTX
Csr2011 june17 15_15_kaminski
PDF
Csr2011 june17 12_00_morin
PDF
Csr2011 june17 11_30_vyalyi
PDF
Csr2011 june17 11_00_lonati
Csr2011 june14 09_30_grigoriev
Csr2011 june18 15_15_bomhoff
Csr2011 june18 15_15_bomhoff
Csr2011 june18 14_00_sudan
Csr2011 june18 15_45_avron
Csr2011 june18 09_30_shpilka
Csr2011 june18 12_00_nguyen
Csr2011 june18 11_00_tiskin
Csr2011 june18 11_30_remila
Csr2011 june17 17_00_likhomanov
Csr2011 june17 16_30_blin
Csr2011 june17 09_30_yekhanin
Csr2011 june17 09_30_yekhanin
Csr2011 june17 12_00_morin
Csr2011 june17 11_30_vyalyi
Csr2011 june17 11_00_lonati
Csr2011 june17 15_15_kaminski
Csr2011 june17 12_00_morin
Csr2011 june17 11_30_vyalyi
Csr2011 june17 11_00_lonati

Csr2011 june17 14_00_bulatov

  • 1. CSP: Algorithms and Dichotomy Conjecture Andrei A. Bulatov Simon Fraser University
  • 2. Constraint Satisfaction Problem I CSP(  ) Definition: Instance: ( V ; A ; C ) where  V is a finite set of variables  A is a set of values  C is a set of constraints Question: whether there is h : V  A such that, for any i , is true where each belongs to 
  • 3. Constraint Satisfaction Problem II u - v - w - x - y - Q ( u,v,w ) R ( w,x ) R ( x,y ) S ( y,u )
  • 4. 3-COL = CSP(  ) Examples: 3-COL u v w x
  • 5. Examples: Linear Equations, SAT Linear Equations : 3-SAT = CSP( ) :
  • 6. Invariants and Polymorphisms Pol(  ) denotes the set of all polymorphisms of relations from  Definition A relation R is invariant with respect to an n - ary operation f (or f is a polymorphism of R ) if, for any tuples the tuple obtained by applying f coordinate-wise is a member of R
  • 7. Affine relations: Relations that can be represented by a system of linear equations Let also ( affine operation) Polymorphisms: Affine Relations If are solutions then
  • 8. 2-clauses give rise to binary relations Let ( median operation) Operation h is a polymorphism of Polymorphisms: 2-SAT
  • 9. does not have any polymorphisms except for very trivial ones, e.g. f ( x,y,z ) =y Polymorphisms: 3-COL
  • 10. Polymorphisms and Complexity Theorem ( Jeavons; 1998 ) If  ,  are constraint languages such that Pol(  )  Pol(  ), then CSP (  ) is log space reducible to CSP (  ) 1 2 2 1 2 1 Larose, Tesson, 2007: This reduction can be made
  • 11. A semilattice operation is a binary operation  satisfying the equations: x  x = x , x  y = y  x , x  ( y  z ) = ( x  y )  z A semilattice operation induces a partial order: a  b  a  b = b Good Polymorphisms: Semilattice There is always a unique maximal element max( x,y ) gcd( x,y )  0 1 1 2 0 2 1 3 6 4 5
  • 12. Good Polymorphisms: Semilattice u - v - w - x - y -
  • 13. Good Polymorphisms: Semilattice Propagation u - v - w - x - y -
  • 14. A majority operation is a ternary operation h that satisfies the equations h ( x,x,y ) = h ( x,y,x ) = h ( y,x,x ) = x Good Polymorphisms: Majority Chinese Remainder Theorem for Majority Let R be a ( k -ary) relation invariant under a majority operation, and is some tuple. Then if for any i,j  {1,..., k } there is a tuple such that then
  • 15. Good Polymorphisms: Majority Propagation again: 2-consistency Any 2-consisted instance has a solution u - v - w - x - y -
  • 16. An affine operation is a ternary operation m that is given by x – y + z where +, – are operations of a certain Abelian group CSP over a language invariant under an affine operation is just solving systems of linear equations Gaussian Elimination Good Polymorphisms: Affine
  • 17. CSP(  ) is Boolean if  is over {0,1} Complexity: Boolean CSP Theorem (Schaefer 1978) For a constraint language  over {0,1} the problem CSP(  ) is solvable in poly time iff  has a semilattice, majority, or affine polymorphism; otherwise it is NP-complete Fine Print: `Trivial’ languages are excluded from the theorem. These are so-called 0- or 1-valid languages, in which every instance has a solution
  • 18. If  consists of a single binary relation (thought of being the edge relation of some (di)graph H), then CSP(  ) is also called H-Coloring Complexity: Graphs Theorem (Hell, Nesetril 1990) For a graph H the H-Coloring problem is solvable in poly time iff E(H) has a majority polymorphism; otherwise it is NP-complete Fine Print: Graphs here must be cores. Then a core has a majority polymorphism iff it is a loop or an interval
  • 19. 5 types of local structure. Defined by the presence of polymorphisms that locally act as on of the 3 good polymorphisms unary none affine only affine boolean all three lattice majority, semilattice semilattice semilattice Types Fine Print : One needs to be quite creative to relate this definition to the actual definition as it was introduced in universal algebra 25 years ago. It is good enough for our purpose, though
  • 20. omits a type if it does not exhibit local structure of this type, otherwise it admits it (Dichotomy Conjecture) CSP(  ) is solvable in polynomial time iff  omits the unary type; NP-complete otherwise CSP(  ) is in NL iff  omits the unary, affine, and semilattice types CSP(  ) is in L iff  omits the unary, affine, lattice and semilattice types; NL-complete otherwise CSP(  ) is in Mod L iff  omits the unary, lattice, and semilattice types Conjectures  p
  • 21. B., Jeavons, Krokhin 2006: if  admits the unary type, CSP(  ) is NP-complete Jeavons et al. mid 90s: algorithms if  has one of the 3 polymorphisms Barto, Kozik 2010; B. 2010: propagation works iff  omits the unary and affine type Idziak, et al. 2010: the Generalized Gaussian elimination algorithm if  omits the unary and semilattice types (+ some extra conditions) ongoing, many people: languages that admit the semilattice and affine types Algorithms
  • 22. Schaefer 1978:  over a 2-element set Hell, Nesetril 1990:  = { E }, where E is binary symmetric 2006:  over a 3-element set Markovic, McKenzie >2011:  over a 4-element case 1 case out of left (as of last Wednesday) B. 2003:  conservative, that is, it contains all unary relations Dichotomy results
  • 23. Polymorphisms of conservative languages If is a polymorphism of a conservative language  , then for any We look at how polymorphisms behave on 2-element subsets If for some 2-elemen subset B there is no polymorphism that is good on B then CSP(  ) is NP-complete Theorem (B. 2003) CSP(  ) for a conservative  on A is poly time iff for any 2-element B  A there is f  Pol(  ) which is affine, majority, or semilattice; otherwise CSP(  ) is NP-complete.
  • 24. Edge coloured graphs G (  ) : Since semilattice operation induces an order, red edges are directed semilattice operation majority operation affine operation
  • 25. Let  be a conservative language over set A B  A is called an as-component (affine-semilattice) if it is minimal with respect to the property: there is no affine or semilattice (directed) edge in G (  ) sticking out of B AS-components The remaining edges are majority
  • 26. Let R be a k -ary relation on A , let be as-components Positions i and j are - related if for any iff I  { 1,...,k } is a coherent set w.r.t. as-components if any i,j  I are -related Coherent Sets
  • 27. CRT for AS-Components Chinese Remainder Theorem for AS-Component Let R   for a conservative  on A and as-components such that for any i,j  { 1,...,k } there is a tuple such that Then there is such that for all i,j  { 1,...,k }.
  • 28. Rectangularity Rectangularity Lemma Let R   and as-components such that Let also be the partition of { 1,...,k } into coherent sets w.r.t. and Then
  • 29. The Algorithm On input ( V,D , C ) run 2-consistency algorithm find as-components such that for any v,w  V as-components are consistent find the coherent sets for each coherent set W solve the problem restricted to W and if all such problem has a solution, any combination of such solutions gives a solution to the problem otherwise remove elements the failed as-components and start over u - v - w - x - y -
  • 30. Ingredients: the graph Chinese Remainder Theorem rectangularity solving smaller problems failed components removal General Case I
  • 31. The graph Cannot get a complete graph, but connected is possible Semilattice and majority edges are defined in almost the same way: a,b if there is a polymorphism which is semilattice or majority on { a,b } Affine edges: Instead of pairs of elements use subset B  A and a partition of B such that there is a polymorphism that acts as an affine operation on the set General Case II A B
  • 32. Chinese Remainder Theorem : holds Rectangularity : does not hold, need a weaker condition Solving smaller problems General Case III Theorem There is a poly time algorithm such that on ( V,A , C ) - if for each v  V and any element a from an as-component there is a solution  with  ( v ) = a , then the algorithm finds a solution; - otherwise it identifies which elements from as-components are not a part of a solution.
  • 33. Failed components removal General Case IV Have to check every element if it is a part of a solution, not only maximal ones u - v - w - x - y -
  • 34. Current Score: 3 : 2 Thank you! Conclusion