**What Is Convex Hull in Data Structure?**

The convex hull is an essential concept in computational geometry and data structures. It is a fundamental algorithm used to determine the smallest convex polygon that encompasses a given set of points or objects.

The convex hull can be visualized as the shape formed by wrapping a rubber band around the outermost points, creating a boundary.

## The Importance of Convex Hull

The convex hull has various applications in different fields, including computer graphics, pattern recognition, image processing, robotics, and geographic information systems. By finding the convex hull of a set of points or objects, we gain valuable insights into their spatial relationships and can efficiently solve problems related to proximity, containment, or connectivity.

## Convex Hull Algorithms

Several algorithms have been developed to compute the convex hull efficiently. Here are some popular ones:

**Graham’s Scan:**This algorithm scans the points counterclockwise and builds the convex hull incrementally.**Jarvis’s March:**Also known as the gift wrapping algorithm, it iteratively selects the point with the smallest polar angle until it completes a full loop.**QuickHull:**This divide-and-conquer algorithm recursively finds extreme points and constructs the convex hull from them.**Chan’s Algorithm:**Combining Graham’s Scan with divide-and-conquer techniques, this algorithm achieves an optimal time complexity.

## The Convex Hull Problem

Given a set of points in either two or three dimensions, finding their convex hull involves determining which points form its vertices. The resulting convex polygon may have edges or facets that connect these vertices, forming the hull’s boundary.

### Convex Hull in Two Dimensions

When working with points in two dimensions, the convex hull is a polygon. The algorithm begins by identifying the point with the lowest y-coordinate (or leftmost if there are multiple points) as the starting point.

Then, it sorts the remaining points based on their polar angle relative to the starting point and scans counterclockwise to build the convex hull.

### Convex Hull in Three Dimensions

In three dimensions, the convex hull becomes a polyhedron. The algorithm selects an initial face composed of three non-collinear points and adds all other points that are visible from this face to an active list.

It iteratively expands this list until no more points can be added, resulting in the complete convex hull.

## Conclusion

The convex hull is a powerful concept in computational geometry and data structures. Its efficient algorithms allow us to determine the smallest convex polygon or polyhedron enclosing a set of points or objects.

By understanding how these algorithms work, we can utilize the convex hull to solve a wide range of problems in various domains effectively.