👉 Cutting math, also known as computational geometry or discrete mathematics applied to geometric problems, involves developing efficient algorithms and data structures to solve complex geometric questions. It focuses on optimizing space and time complexity, often using techniques like triangulation, Voronoi diagrams, and convex hull algorithms. For instance, Delaunay triangulation efficiently connects points in a plane to form triangles that maximize minimum angle, useful in mesh generation and finite element analysis. Similarly, algorithms for finding nearest neighbors or computing geometric invariants (like area and perimeter) are crucial in computer graphics, robotics, and geographic information systems. These methods enable solutions to problems that would otherwise be computationally infeasible, bridging the gap between geometric intuition and algorithmic precision.
