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Extended Constraint Satisfaction Problems


Joanna Ochremiak


Warsaw, 24th February 2016

3-colorability




Question: Can we color this graph?

3-colorability




Question: Can we color this graph?

3-colorability




Question: Can we color this graph?

3-colorability




Question: Can we color this graph?

Constraint Satisfaction Problem


Question: Can we assign values to variables so that all the constraints are satisfied?

Complexity of CSP


A CSP template Γ is a set of types of constraints.


Problem: CSP(Γ)

Input: CSP instance with constraints from Γ

Decide: Is there an assignment satisfying all the constraints?


CSP Dichotomy Conjecture. For every finite template Γ, the problem CSP(Γ) is solvable in polynomial time or NP-complete.

Max-cut


cost = 5 cost = 2

Goal: Find a coloring with minimal cost.

Valued CSP

Valued CSP


Goal: Find an assignment with minimal cost.

2-colorability

Goal: Find a coloring with minimal cost.

Complexity of VCSP


Problem: VCSP(Γ)

Input: VCSP instance with (valued) constraints from Γ

Goal: Find an assignment with minimal cost.


VCSP Dichotomy Conjecture. For every finite set of types of (valued) constraints Γ, the problem VCSP(Γ) is solvable in polynomial time or NP-hard.


Theorem (Kozik, O.). If Pol+(Γ) does not have a cyclic operation, then VCSP(Γ) is NP-hard.

3-colorability


Question: Can we color this graph?

3-colorability


Question: Can we color this graph?

FO-definable Instance


{ab | a,bA,ab} - vertices


{{ab,bc} | a,b,cA,abbcac} - edges

Definable CSP

Definable CSP


Problem: def-CSP(Γ)

Input: FO-definable CSP instance over Γ

Decide: Is there an assignment satisfying all the constraints?


A template Γ is locally finite if every constraint allows only finitely many values for each variable.


Theorem (Klin, Kopczyński, O., Toruńczyk). If Γ is locally finite, then def-CSP(Γ) is decidable.


TM with Atoms


I ab bc eb ae eb A I ab bc R ab bc eb ae eb B B ... ab bc bc ce ad B B ... language of paths States: I, ab, bc,... , A, R

Turing machine with atoms is a Turing machine whose alphabet, state space, and transition relation are FO-definable sets.

Standard Alphabets


An alphabet A is standard iff every language L over A recognisable by a nondeterministic TMA is recognisable by some deterministic TMA.


Theorem (Bojańczyk, Klin, Lasota, Toruńczyk). There exists a non-standard alphabet.


Theorem (Klin, Lasota, O., Toruńczyk). An alphabet A is standard iff a CSP template ΓA has a majority polymorphism.

Polymorphisms


A function f:DkD is a polymorphism of a relation RDn if

x1=(x11,,xn1)Rxk=(x1k,,xnk)R(f(x11,,x1k),,f(xn1,,xnk))R
A CSP template Γ is a set of relations over a set of values D.

A function f:DkD is a polymorphism of a template Γ if it is a polymorphism of every relation in Γ.

Majority || Complexity || Weighted Polymorphisms

Complexity of CSP



Theorem (Bulatov, Krokhin, Jeavons). If every polymorphism of Γ is a polymorphism of R, then CSP(Γ{R}) is polynomial-time reducible to CSP(Γ).

Polymorphisms characterize the complexity of CSP.


Weighted Polymorphisms || Complexity of VCSP

Complexity of VCSP



Theorem (Cohen, Cooper, Creed, Jeavons, Živný). If every weighted polymorphism of Γ is a weighted polymorphism of R, then VCSP(Γ{R}) is polynomial-time reducible to VCSP(Γ).

Weighted polymorphisms characterize the complexity of VCSP.


Weighted Polymorphisms

Weighted Polymorphisms


Let R : DnQ{} be a weighted relation.


A weighted polymorphism of R is a probability distribution ω on the set of m-ary functions such that for any n-tuples x1,,xm, such that R(xi)<, g  supp(ω)ω(g)R(g(x1,,xm))1m(R(x1)++R(xm)), where supp(ω) is the set of operations to which ω assigns a positive probability.

Polymorphisms || Complexity of VCSP

Non-standard Alphabet


{(a,c,e),(a,d,f),(b,c,f),(b,d,e)}