scholarly journals Tight Complexity Lower Bounds for Integer Linear Programming with Few Constraints

2020 ◽  
Vol 12 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Dušan Knop ◽  
Michał Pilipczuk ◽  
Marcin Wrochna
Keyword(s):  
Linear Programming ◽  
Lower Bounds ◽  
Integer Linear Programming ◽  
Complexity Lower Bounds

scholarly journals Optimization of a k-covering of a bounded set with circles of two given radii

2021 ◽  
Vol 11 (1) ◽  
pp. 232-240
Author(s):  
Alexander V. Khorkov ◽  
Shamil I. Galiev
Keyword(s):  
Linear Programming ◽  
Numerical Method ◽  
Lower Bounds ◽  
Integer Linear Programming ◽  
Numerical Results ◽  
Programming Models ◽  
Bounded Set ◽  
The Given

Abstract A numerical method for investigating k-coverings of a convex bounded set with circles of two given radii is proposed. Cases with constraints on the distances between the covering circle centers are considered. An algorithm for finding an approximate number of such circles and the arrangement of their centers is described. For certain specific cases, approximate lower bounds of the density of the k-covering of the given domain are found. We use either 0–1 linear programming or general integer linear programming models. Numerical results demonstrating the effectiveness of the proposed methods are presented.

scholarly journals Tournaments as Feedback Arc Sets

10.37236/1214 ◽  
1995 ◽  
Vol 2 (1) ◽  
Author(s):  
Garth Isaak
Keyword(s):  
Linear Programming ◽  
Lower Bounds ◽  
Integer Linear Programming ◽  
Minimum Size ◽  
Programming Formulation ◽  
Binary Expansion ◽  
Feedback Arc Set ◽  
Disjoint Cycles ◽  
Linear Programming Formulation ◽  
Integer Linear Programming Formulation

We examine the size $s(n)$ of a smallest tournament having the arcs of an acyclic tournament on $n$ vertices as a minimum feedback arc set. Using an integer linear programming formulation we obtain lower bounds $s(n) \geq 3n - 2 - \lfloor \log_2 n \rfloor$ or $s(n) \geq 3n - 1 - \lfloor \log_2 n \rfloor$, depending on the binary expansion of $n$. When $n = 2^k - 2^t$ we show that the bounds are tight with $s(n) = 3n - 2 - \lfloor \log_2 n \rfloor$. One view of this problem is that if the 'teams' in a tournament are ranked to minimize inconsistencies there is some tournament with $s(n)$ 'teams' in which $n$ are ranked wrong. We will also pose some questions about conditions on feedback arc sets, motivated by our proofs, which ensure equality between the maximum number of arc disjoint cycles and the minimum size of a feedback arc set in a tournament.

scholarly journals Security Analysis of SKINNY under Related-Tweakey Settings

2017 ◽  
pp. 37-72 ◽  
Author(s):  
Guozhen Liu ◽  
Mohona Ghosh ◽  
Ling Song
Keyword(s):  
Linear Programming ◽  
Lower Bounds ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Security Analysis ◽  
Block Size ◽  
Differential Cryptanalysis ◽  
Mixed Integer ◽  
New Family ◽  
Searching Method

In CRYPTO’16, a new family of tweakable lightweight block ciphers - SKINNY was introduced. Denoting the variants of SKINNY as SKINNY-n-t, where n represents the block size and t represents the tweakey length, the design specifies t ∈ {n, 2n, 3n}. In this work, we evaluate the security of SKINNY against differential cryptanalysis in the related-tweakey model. First, we investigate truncated related-tweakey differential trails of SKINNY and search for the longest impossible and rectangle distinguishers where there is only one active cell in the input and the output. Based on the distinguishers obtained, 19, 23 and 27 rounds of SKINNY-n-n, SKINNY-n-2n and SKINNY-n-3n can be attacked respectively. Next, actual differential trails for SKINNY under related-tweakey model are explored and optimal differential trails of SKINNY-64 within certain number of rounds are searched with an indirect searching method based on Mixed-Integer Linear Programming. The results show a trend that as the number of rounds increases, the probability of optimal differential trails is much lower than the probability derived from the lower bounds of active Sboxes in SKINNY.

scholarly journals Unary Integer Linear Programming with Structural Restrictions

2018 ◽  
Author(s):  
Eduard Eiben ◽  
Robert Ganian ◽  
Dušan Knop ◽  
Sebastian Ordyniak
Keyword(s):  
Linear Programming ◽  
Lower Bounds ◽  
Integer Linear Programming ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Graph Representation ◽  
Input Size ◽  
Variable Domains ◽  
Fixed Constant ◽  
Variable Interactions

Recently a number of algorithmic results have appeared which show the tractability of Integer Linear Programming (ILP) instances under strong restrictions on variable domains and/or coefficients (AAAI 2016, AAAI 2017, IJCAI 2017). In this paper, we target ILPs where neither the variable domains nor the coefficients are restricted by a fixed constant or parameter; instead, we only require that our instances can be encoded in unary. We provide new algorithms and lower bounds for such ILPs by exploiting the structure of their variable interactions, represented as a graph. Our first set of results focuses on solving ILP instances through the use of a graph parameter called clique-width, which can be seen as an extension of treewidth which also captures well-structured dense graphs. In particular, we obtain a polynomial-time algorithm for instances of bounded clique-width whose domain and coefficients are polynomially bounded by the input size, and we complement this positive result by a number of algorithmic lower bounds. Afterwards, we turn our attention to ILPs with acyclic variable interactions. In this setting, we obtain a complexity map for the problem with respect to the graph representation used and restrictions on the encoding.

scholarly journals New Bounds on Roller Coaster Permutations

2021 ◽  
Author(s):  
Fábio Botler ◽  
Bruno L. Netto
Keyword(s):  
Linear Programming ◽  
Lower Bounds ◽  
Integer Linear Programming ◽  
Programming Model ◽  
Linear Programming Model ◽  
Roller Coaster ◽  
Integer Linear Programming Model

A roller coaster is a permutation \pi that maximizes the sum $\t(\pi) = \sum_{\tau\in X(\pi)}\id(\tau)$, where \(X(\pi)\) denotes the set of subsequences of \(\pi\) with cardinality at least \(3\); and \(\id(\tau)\) denotes the number of maximal increasing or decreasing subsequences of contiguous numbers of \(\tau\). We denote by \(\t_{\max}(n)\) the value \(\t(\pi)\), where \(\pi\) is a roller coaster of \(\{1,\ldots,n\}\), for \(n\geq 3\). Precise values of \(\t_{\max}(n)\) for \(n\leq 13\) were presented in \cite{AhmedTanbir}. In this paper, we explore the problem of computing lower bounds for \(\t_{\max}(n)\). More specifically, we present a cubic algorithm to compute $\t(\pi)$ for any given permutation $\pi$; and an Integer Linear Programming model to obtain roller coasters. As a result, we improve known lower bounds found in the literature for $n \leq 40$.

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