A convex hull is a fundamental concept in computational geometry, and it refers to the smallest convex polygon or polyhedron that encloses a set of points or other geometric objects in space. In two dimensions (2D), it is a convex polygon, while in three dimensions (3D), it is a convex polyhedron. The convex hull problem involves finding this smallest convex shape that encompasses the given set of points.

Key characteristics and details about convex hulls include:

*Convexity:* The convex hull is always convex, meaning that for any two points within the hull, the line segment connecting them lies entirely within the hull. This property ensures that the hull does not have any "dents" or concave portions.

*Efficiency:* There are several algorithms for computing the convex hull of a set of points, with the most famous being the Graham's scan algorithm, the Jarvis march (gift wrapping) algorithm, and the QuickHull algorithm. These algorithms have varying time complexities, with some being more efficient for specific scenarios or data distributions.

*Applications:* Convex hulls have applications in various fields, including computer graphics, geographic information systems (GIS), pattern recognition, robotics, and computer-aided design. For example, in GIS, they are used to find the boundary of a geographical region, while in robotics, they help plan collision-free paths for robots.

*Complexity:* The computational complexity of finding the convex hull depends on the algorithm used and the number of points in the input set. In the worst case, the complexity can be O(n^2) or O(n*log(n)), where 'n' is the number of input points.

*Incremental and Divide-and-Conquer Approaches:* Convex hull algorithms can be broadly categorized into two types: incremental and divide-and-conquer. Incremental algorithms build the hull incrementally by adding points one by one, while divide-and-conquer algorithms divide the set of points into smaller subsets, compute their convex hulls, and then merge them to find the overall convex hull.

*Higher Dimensions:* While the concept of a convex hull is most commonly associated with 2D and 3D spaces, it can be extended to higher dimensions as well, where it involves finding the smallest convex polytope that encloses a set of points.

Overall, the convex hull problem is a fundamental problem in computational geometry with a wide range of practical applications, and it has been extensively studied, leading to the development of efficient algorithms for its computation.

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