Efikasni algoritmi za probleme iz diskretne geometrije

The first class of problem we study deals with geometric matchings. Given a set of points in the plane, we study perfect matchings of those points by straight line segments so that the segments do not cross. Bottleneck matching is such a matching that minimizes the length of the longest segment. We are interested in finding a bottleneck matching of points in convex position. In the monochromatic case, where any two points are allowed to be matched, we give an O(n 2 )-time algorithm for finding a bottleneck matching, improving upon previously best known algorithm of O(n 3 ) time complexity. We also study a bichromatic version of this problem, where each point is colored either red or blue, and only points of different color can be matched. We develop a range of tools, for dealing with bichromatic non-crossing matchings of points in convex position. Combining that set of tools with a geometric analysis enable us to solve the problem of finding a bottleneck matching in O(n 2 ) time. We also design an O(n)-time algorithm for the case where the given points lie on a circle. Previously best known results were O(n 3 ) for points in convex position, and O(n log n) for points on a circle. The second class of problems we study deals with dilation of geometric networks. Given a polygon representing a network, and a point p in the same plane, we aim to extend the network by inserting a line segment, called a feed-link, which connects p to the boundary of the polygon. Once a feed link is fixed, the geometric dilation of some point q on the boundary is the ratio between the length of the shortest path from p to q through the extended network, and their Euclidean distance. The utility of a feed-link is inversely proportional to the maximal dilation over all boundary points. We give a linear time algorithm for computing the feed-link with the minimum overall dilation, thus improving upon the previously known algorithm of complexity that is roughly O(n log n).Prva klasa problema koju proučavamo tičee se geometrijskih mečinga. Za dat skup tačaaka u ravni, posmatramo savršene mečinge tih tačaka spajajućii ih  dužima koje   se ne smeju sećui. Bottleneck mečing je takav mečing koji minimizuje dužinu najduže duži. Naš cilj je da nađemo bottleneck mečiing tačaka u konveksnom položaju.Za monohromatski slučaj, u kom je dozvoljeno upariti svaki par tačaka, dajemo algoritam vremenske složenosti O(n 2) za nalaženje bottleneck mečinga. Ovo  je bolje od prethodno najbolji poznatog algoritam, čiija je složenost O(n 3 ). Takođe proučavamo bihromatsku verziju ovog problema, u kojoj je svaka tačka  obojena ili u crveno ili u plavo, i dozvoljeno je upariti samo tačke različite boje. Razvijamo niz alata za rad sa bihromatskim nepresecajućim mečinzima tačaka u konveksnom položaju. Kombinovanje ovih alata sa geometrijskom analizom omogućava nam da rešimo problem nalaženja bottleneck mečinga u O(n 2 ) vremenu. Takođe, konstruišemo algoritam vremenske složenosti O(n) za slučaj kada  sve date tačkke leže na krugu. Prethodno najbolji poznati algoritmi su imali složenosti  O(n 3 ) za tačkeke u konveksnom položaju i O(n log n) za tačke na krugu. Druga klasa problema koju proučaavamo tiče se dilacije u geometrijskim mrežama. Za datu mrežu predstavljenu poligonom, i tačku p u istoj ravni, želimo proširiti mrežu  dodavanjem duži zvane feed-link koja povezuje p sa obodom poligona. Kada je feed- link fiksiran, definišemo geometrijsku dilaciju neke tačke q na obodu kao odnos izme  đu  dužine najkraćeg puta od p do q kroz proširenu mrežu i njihovog Euklidskog rastojanja. Korisnost feed-linka je obrnuto proporcionalna najvećoj dilaciji od svih ta čaka na obodu poligona. Konstruišemo algoritam linearne vremenske složenosti koji nalazi feed-link sa najmanom sveukupnom dilacijom. Ovim postižemo bolji rezultat od prethodno najboljeg poznatog algoritma složenosti približno O(n log n)

New Applications of the Nearest-Neighbor Chain Algorithm

The nearest-neighbor chain algorithm was proposed in the eighties as a way to speed up certain hierarchical clustering algorithms. In the first part of the dissertation, we show that its application is not limited to clustering. We apply it to a variety of geometric and combinatorial problems. In each case, we show that the nearest-neighbor chain algorithm finds the same solution as a preexistent greedy algorithm, but often with an improved runtime. We obtain speedups over greedy algorithms for Euclidean TSP, Steiner TSP in planar graphs, straight skeletons, a geometric coverage problem, and three stable matching models. In the second part, we study the stable-matching Voronoi diagram, a type of plane partition which combines properties of stable matchings and Voronoi diagrams. We propose political redistricting as an application. We also show that it is impossible to compute this diagram in an algebraic model of computation, and give three algorithmic approaches to overcome this obstacle. One of them is based on the nearest-neighbor chain algorithm, linking the two parts together

Discrete Geometry (hybrid meeting)

A number of important recent developments in various branches of discrete geometry were presented at the workshop, which took place in hybrid format due to a pandemic situation. The presentations illustrated both the diversity of the area and its strong connections to other fields of mathematics such as topology, combinatorics, algebraic geometry or functional analysis. The open questions abound and many of the results presented were obtained by young researchers, confirming the great vitality of discrete geometry

Algorithms and Data Structures for Faster Nearest-Neighbor Classification

Given a set P of n labeled points in a metric space (X,d), the nearest-neighbor rule classifies an unlabeled query point q ∈ X with the class of q's closest point in P. Despite the advent of more sophisticated techniques, nearest-neighbor classification is still fundamental for many machine-learning applications. Over the years, this~has motivated numerous research aiming to reduce its high dependency on the size and dimensionality of the data. This dissertation presents various approaches to reduce the dependency of the nearest-neighbor rule from n to some smaller parameter k, that describes the intrinsic complexity of the class boundaries of P. This is of particular significance as it is usually assumed that k ≪ n on real-world training sets. One natural way to achieve this dependency reduction is to reduce the training set itself, selecting a subset R ⊆ P to be used by the nearest-neighbor rule~to~answer incoming queries, instead of using P. Evidently, this approach would reduce the dependencies of the nearest-neighbor rule from n, the size of P, to the size of R. This dissertation explores different techniques to select subsets whose sizes are proportional to k, and that provide varying degrees of correct classification guarantees. Another alternative involves bypassing training set reduction, and instead building data structures designed to answer classification queries directly. To this end, this dissertation proposes the Chromatic AVD; a Quadtree-based data structure designed to answer ε-approximate nearest-neighbor classification queries. The query time and space complexities of this data structure depend on k_ε; a generalization of k that describes the intrinsic complexity of the ε-approximate class boundaries of P