Robust algorithms with polynomial loss for near-unanimity CSPs

An instance of the Constraint Satisfaction Problem (CSP) is given by a family of constraints on overlapping sets of variables, and the goal is to assign values from a xed domain to the variables so that all constraints are satised. In the optimization version, the goal is to maximize the number of satised constraints. An approximation algorithm for CSP is called robust if it outputs an assignment satisfying an (1????g("))-fraction of constraints on any (1????")-satisable instance, where the loss function g is such that g(") ! 0 as " ! 0. We study how the robust approximability of CSPs depends on the set of constraint relations allowed in instances, the so-called constraint language. All constraint languages admitting a robust polynomial-time algorithm (with some g) have been characterised by Barto and Kozik, with the general bound on the loss g being doubly exponential, specically g(") = O((log log(1="))= log(1=")). It is natural to ask when a better loss can be achieved: in particular, polynomial loss g(") = O("1=k) for some constant k. In this paper, we consider CSPs with a constraint language having a nearunanimity polymorphism. This general condition almost matches a known necessary condition for having a robust algorithm with polynomial loss. We give two randomized robust algorithms with polynomial loss for such CSPs: one works for any near-unanimity polymorphism and the parameter k in the loss depends on the size of the domain and the arity of the relations in ????, while the other works for a special ternary near-unanimity operation called dual discriminator with k = 2 for any domain size. In the latter case, the CSP is a common generalisation of Unique Games with a xed domain and 2-Sat. In the former case, we use the algebraic approach to the CSP. Both cases use the standard semidenite programming relaxation for CSP

The Combined Basic LP and Affine IP Relaxation for Promise VCSPs on Infinite Domains

Convex relaxations have been instrumental in solvability of constraint satisfaction problems (CSPs), as well as in the three different generalisations of CSPs: valued CSPs, infinite-domain CSPs, and most recently promise CSPs. In this work, we extend an existing tractability result to the three generalisations of CSPs combined: We give a sufficient condition for the combined basic linear programming and affine integer programming relaxation for exact solvability of promise valued CSPs over infinite-domains. This extends a result of Brakensiek and Guruswami [SODA\u2720] for promise (non-valued) CSPs (on finite domains)

The combined basic LP and affine IP relaxation for promise VCSPs on infinite domains

Convex relaxations have been instrumental in solvability of constraint satisfaction problems (CSPs), as well as in the three different generalisations of CSPs: valued CSPs, infinite-domain CSPs, and most recently promise CSPs. In this work, we extend an existing tractability result to the three generalisations of CSPs combined: We give a sufficient condition for the combined basic linear programming and affine integer programming relaxation for exact solvability of promise valued CSPs over infinite-domains. This extends a result of Brakensiek and Guruswami [SODA'20] for promise (non-valued) CSPs (on finite domains).Comment: Full version of an MFCS'20 pape

Absorption in Universal Algebra and CSP

The algebraic approach to Constraint Satisfaction Problem led to many developments in both CSP and universal algebra. The notion of absorption was successfully applied on both sides of the connection. This article introduces the concept of absorption, illustrates its use in a number of basic proofs and provides an overview of the most important results obtained by using it

Some Results for FO-definable Constraint Satisfaction Problems Described by Digraph Homomorphisms

Constraint satisfaction problems, or CSPs, are a naturally occurring class of problems which involve assigning values to variables while respecting a set of constraints. When studying the computational and descriptive complexity of such problems it is convenient to use the equivalent formulation, introduced by Feder and Vardi, that CSPs are homomorphism problems. In this context we ask if there exists a homomorphism to some target structure. Using this view many tools and ideas have been introduced in combinatorics, logic and algebra for studying the complexity of CSPs. In this thesis we concentrate on combinatorics and give characterization results based on digraph properties. Where previous studies focused on CSPs defined by a single digraph with lists we extend our relational structures to consist of many binary relations which each individually describe a distinct digraph on the structures universe. A majority of our results are obtained by using an algorithm introduced by Larose, Loten and Tardif which determines whether a structure defines a CSP whose homomorphism problem can be represented by first order logic. Using this tool we begin by completely classifying which of these structures are FO-definable when each of the relations defines a transitive tournament. We then generalize a characterization theorem, first given by Lemaître, to include structures containing any finite number of digraph relations and lists. We conclude with examples of obstructions and properties that can determine if a particular relational structure has a CSP which is FO-definable and how to construct such structures