Design and Study of Scalable Algorithms for Exploration, Search, and Detection in Large Grids

This research explores the design and implementation of scalable algorithms for efficient exploration, search, and detection in large grids. The study introduces three distinct yet complementary methods: R.E.B.S (Rhythmic Expansion Batch Search), G.E.B.S (Gradient Expansion Batch Search), and L.E.B.S (Learning Exploration Batch Search). R.E.B.S employs a radial expansion strategy, isolating newly explored regions from previous iterations to refine search precision. G.E.B.S divides the grid into core and gradient areas, applying structured and adaptive search techniques to balance efficiency and depth. L.E.B.S leverages learning-based approaches, using sampled submatrices to generate global search masks for iterative refinement. These algorithms offer scalable solutions for handling vast datasets, whether used independently or in synergy, making them applicable to various problem domains requiring structured exploration and pattern detection.