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Computational geometry is used widely throughout linear programming, this is a way of formulating real-world problems into mathematical models and then optimising it.

Some classical problems are (1) computing the convex hull of a set of points, (2) determining the intersection points of a set of line segments, and (3) triangulating a simple polygon. While the focus is theoretical and algorithmic, the field of computational geometry does deal with geometric objects.

Other important applications of computational geometry include robotics (motion planning and visibility problems), geographic information systems (GIS) (geometrical location and search, route planning), integrated circuit design (IC geometry design and verification), computer-aided engineering (CAE) (mesh generation), ...

Computational geometry is largely classified into two major branches: combinatorial computational geometry and numerical computational geometry. The first deals with geometric objects as discrete entities.

Computational geometry is a mathematical field that involves the design, analysis and implementation of efficient algorithms for solving geometric input and output problems. It is sometimes used to refer to pattern recognition and describe the solid modeling algorithms used for manipulating curves and surfaces.

Examples of problems treated by computational geometry include determination of the convex hull and Voronoi diagram for a set of points, triangulation of points in a plane or in space, and other related problems.

In a broader sense computational geometry is concerned with the design and analysis of algorithms for solving geometric problems. In a deeper sense it is the study of the in- herent computational complexity of geometric problems under varying models of computation.