In 1907, Henry Ernest Dudeney posed a puzzle: "cut any equilateral triangle ... into as few pieces as possible that will fit together and form a perfect square" (without overlap, via translation and rotation). Four weeks later, Dudeney demonstrated a beautiful four-piece solution, which today remains perhaps the most famous example of dissection. In this paper (over a century later), we finally solve Dudeney’s puzzle, by proving that the equilateral triangle and square have no common dissection with three or fewer polygonal pieces. We reduce the problem to the analysis of discrete graph structures representing the correspondence between the edges and the vertices of the pieces forming each polygon.

We introduce the Multi-Objective Combinatorial Reconfiguration Optimization Problem (MO-CROP), and propose an Answer Set Programming (ASP) based approach for its solution. MO-CROP involves finding the Pareto-optimal sequences (or Pareto front) of adjacent feasible solutions between two given feasible solutions of a combinatorial problem, considering both cost and length. Our algorithm is compactly implemented through multi-shot ASP solving, and its implementing solver optirecon provides an effective tool for solving MO-CROP. As a concrete example of MO-CROP, we present an ASP encoding for solving the multi-objective independent set reconfiguration optimization problem. Experimental results on the benchmark set from the recent CoRe Challenge demonstrate our approach’s ability to capture diverse optimal sequences that reveal trade-offs between cost and length, a capability often lacking in traditional combinatorial reconfiguration methods.

This study specifically addresses the enumeration problem of the class of weighted trees. A weighted tree is a rooted unordered tree, and each vertex in a weighted tree is assigned an integer weight. The task is to enumerate the weighted trees. Given a tree and a weight , the objective is to enumerate all weighted trees that share the same tree structure as and have a total weight of . Note that the weight assigned to each vertex must be non-negative. The algorithm utilizes reverse search and enumerates each weighted tree in a constant amortized time. By combining our findings with the enumeration of the class of rooted trees, we can efficiently enumerate every weighted tree with a maximum of vertices and a total weight of for any given positive inputs and .

For a given set of pieces and a frame, an anti-slide puzzle asks us to arrange the pieces so that none of the pieces can slide in the frame. Since the first anti-slide puzzle that consists of dozens of cuboid pieces in 3D was invented, tons of anti-slide puzzles using pentominoes have been proposed. Some of them are not in a frame, which we call that interlock puzzles. In this paper, we investigate computational complexity of anti-slide puzzles and interlock puzzles in 2D. In previous work in theoretical computer science, a few models have been proposed for dealing with the notion of anti-slide, however, there exist gaps between these models and real puzzles. We first give mathematical characterizations of anti-slide puzzles and show the relationship between the previous work. Using a mathematical characterization, we give a polynomial time algorithm for determining if a given arrangement of polyominoes is anti-slide or not in a model. Next, we prove that the decision problem whether a given set of polyominoes can be arranged to be anti-slide or not is strongly NP-complete even if every piece is x-monotone. On the other hand, a set of pieces cannot be arranged to be interlocked if all pieces are convex polygons.

A swarm of anonymous oblivious mobile robots, operating in deterministic Look-Compute-Move cycles, is confined within a circular track. All robots agree on the clockwise direction (chirality), they are activated by an adversarial semi-synchronous scheduler (SSYNCH), and an active robot always reaches the destination point it computes (rigidity). Robots have limited visibility: each robot can see only the points on the circle that have an angular distance strictly smaller than a constant $\vartheta$ from the robot's current location, where $0<\vartheta\leqπ$ (angles are expressed in radians). We study the Gathering problem for such a swarm of robots: that is, all robots are initially in distinct locations on the circle, and their task is to reach the same point on the circle in a finite number of turns, regardless of the way they are activated by the scheduler. Note that, due to the anonymity of the robots, this task is impossible if the initial configuration is rotationally symmetric; hence, we have to make the assumption that the initial configuration be rotationally asymmetric. We prove that, if $\vartheta=π$ (i.e., each robot can see the entire circle except its antipodal point), there is a distributed algorithm that solves the Gathering problem for swarms of any size. By contrast, we also prove that, if $\vartheta\leq π/2$, no distributed algorithm solves the Gathering problem, regardless of the size of the swarm, even under the assumption that the initial configuration is rotationally asymmetric and the visibility graph of the robots is connected. The latter impossibility result relies on a probabilistic technique based on random perturbations, which is novel in the context of anonymous mobile robots. Such a technique is of independent interest, and immediately applies to other Pattern-Formation problems.

Given two independent sets I and J of a graph G, imagine that a token (coin) is placed on each vertex in I. Then, the Sliding Token problem asks if one could transforms I to J using a sequence of elementary steps, where each step requires sliding a token from one vertex to one of its neighbors, such that the resulting set of vertices where tokens are placed still remains independent. In this paper, we describe a polynomial-time algorithm for solving Sliding Token in case the graph G is a cactus. Our algorithm is designed based on two observations. First, all structures that forbid the existence of a sequence of token slidings between I and J, if exist, can be found in polynomial time. A no-instance may be easily deduced using this characterization. Second, without such forbidden structures, a sequence of token slidings between I and J does exist.

Abstract We consider the following simple network design problem. The input consists of n weighted nodes, and the output is an edge‐weighted connected network such that the total weight of the edges incident to a node is at least the given weight of the node. We aim to design the cheapest connected network; that is, the reachability of the network should be guaranteed, and the network is better if its total weight is less. In this article, we first show an efficient algorithm that produces an optimal network with minimum weight. The algorithm runs in linear time, and the resulting network contains at most n edges, where n is the number of nodes. To construct a connected network, at least n ‐ 1 edges are required. However, the algorithm sometimes outputs n edges. Next, we aim to minimize not only the weight but also the number of edges. That is, for given n weighted nodes, we aim to design a cheapest tree. Then, the problem becomes \documentclass{article}\usepackage{mathrsfs, amsmath, amssymb}\pagestyle{empty}\begin{document}\begin{align*}\mathcal{N}\mathcal{P}\end{align*} \end{document} ‐complete. We also propose efficient approximation algorithms for constructing a cheapest tree. © 2013 Wiley Periodicals, Inc. NETWORKS, 2013

BACKGROUND: In population-based studies, it is generally recognized that single nucleotide polymorphism (SNP) markers are not independent. Rather, they are carried by haplotypes, groups of SNPs that tend to be coinherited. It is thus possible to choose a much smaller number of SNPs to use as indices for identifying haplotypes or haplotype blocks in genetic association studies. We refer to these characteristic SNPs as index SNPs. In order to reduce costs and work, a minimum number of index SNPs that can distinguish all SNP and haplotype patterns should be chosen. Unfortunately, this is an NP-complete problem, requiring brute force algorithms that are not feasible for large data sets. RESULTS: We have developed a double classification tree search algorithm to generate index SNPs that can distinguish all SNP and haplotype patterns. This algorithm runs very rapidly and generates very good, though not necessarily minimum, sets of index SNPs, as is to be expected for such NP-complete problems. CONCLUSIONS: A new algorithm for index SNP selection has been developed. A webserver for index SNP selection is available at http://cognia.cu-genome.org/cgi-bin/genome/snpIndex.cgi/