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Regular Switching Components

Abstract : We consider a problem of Discrete Tomography which consists of reconstructing a finite lattice set S of Z² from given horizontal and vertical X-rays (i.e. with prescribed numbers of points in each row and column). Without additional requirements, the problem can be solved in polynomial time. Many variants require the solution to be in a chosen class A. For instance, the problem is NP-complete for the class A=HV of HV-convex lattice sets and it is polynomial for the class A = HVP of HV-convex polyominoes. Twenty years after these results, the problem's complexity remains unknown for $\A = \C$, the class of $\C$-convex lattice sets (i.e. two-dimensional lattice polytopes). The main difficulty to solve this problem comes from combinatorial structures called \textit{switching components}. Switching components are closed path with horizontal and vertical edges such that the solutions $S$ are composed of either the elements of even or of odd indices. This binary choice can be encoded by a Boolean variable associated with the switching component and the convexity constraints are simply encoded by SAT clauses (2-clauses for HV-convexity and 3-clauses for C-convexity). The purpose of the paper is to investigate the properties of the switching components and the consequences of the convexity requirements. We divide the switching components in two classes: regular if their turning angle is constant, irregular otherwise. We prove that adjacent regular switching components have the same Boolean values.This property allows us to merge them into extended switching components. We prove that if all switching components are regular, then the extended switching components are all independent (then the number of solutions with the considered feet is $2^n$, where $n$ is the number of extended switching components). Finally, we prove that they are geometrically ordered.
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Submitted on : Monday, February 18, 2019 - 7:32:32 PM
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Yan Gérard. Regular Switching Components. Theoretical Computer Science, Elsevier, 2019, Special issue of TCS devoted to the memory of Maurice Nivat, ⟨10.1016/j.tcs.2019.01.010⟩. ⟨hal-01832674v2⟩

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