Python implementation of algorithm for seeking "Closest pair of points" (divide and conquer).

A Multi object detection and tracking with yolov7 inference.

a QGIS-plugin to calculate the closest points for one layer to another

🐍 Python implementation to find the closest pair of points in a three-dimensional space

Simple GUI for Closest Pair Problem using DnC and BF

Simple closest pair problem solution with python 🧩

Python library for finding the closest pairs in an array

Tugas Kecil 2 - Strategi Algoritma 2022/2023

Geometry algorithms implementations in Python

Tugas Kecil 2 Strategi Algoritma IF2211 - Closest Pair Point using Divide and Conquer Algorithm

Closest pair problem adalah masalah dalam geometri komputasi yang mencari pasangan titik terdekat dari sekumpulan titik di suatu ruang. Ada beberapa algoritma yang dikembangkan untuk menyelesaikan masalah ini dengan lebih efisien, seperti algoritma divide-and-conquer.

Projeto e implementação de uma classe e funções para resolução de problemas clássicos de geometria computacional. Trabalho realizado no 6º período de Ciência da Computação do Instituto Federal de Minas Gerais(IFMG) - Campus Formiga para a Disciplina Projeto e Análise de Algoritmos

A Closest Pair of Point Problem Solver, made using the Divide and Conquer approach for the Algorithm and Strategies Course using Python

Given an array consisting of n distinct points[p1[x1, y1] ... pn[xn, yn]]. Find the closest pair of points using a Divide-and-Conquer Algorithm with asymptotic complexity of O(N log N)

Tucil 2 IF2211 Algorithm Strategies: Python implementation to search closest pair of points in d dimensions

Finding Closest Pair of Points in Multi-Dimensional Space using Divide and Conquer Algorithm

Brute Force to find the minimum distance between 2 points in a set of n points takes (n^2) computations. Can we do better?

Closest Pair Problem in 3D using Divide and Conquer

An advanced and comprehensive implementation of the Closest Pair of Points problem using the Divide and Conquer algorithm in computational geometry. This project provides an in-depth exploration of efficient algorithms for solving proximity problems in a two-dimensional plane, focusing on optimizing performance for large-scale datasets.