Mastering Computational Geometry Algorithms with C++
What you'll learn
- Applications of Computational Geometry algorithms.
- Able to implement Computation Geometry algorithms with C++
- Precision and Performance concern and how to solve them when implementing Computational Geometry Algorithms
- Solid understanding on Mathematics and the interpretation of Mathematics behind Computational Geometry
- Solid understanding on Vector algebra to be using in Computational Geometry.
- Knowledge on basic data structures and algorithms will be helpful.
- Hands on experience with any programming language.
Computational Geometry algorithms have tons of applications in the fields like computer games, computer simulation, computer graphic, CAD/CAM software's, Navigation systems and many more day to day applications. But the data structure and algorithms fall under this category is still considered specialized area due to inherit complexities of those. To become fluent in computational geometry you need at least following knowledge.
Through knowledge on linear algebra and geometrical representation of those.
Mathematical representation of geometrical shapes.
Computational steps for primitive test like intersection and distance queries.
Good understanding on algorithms in computational geometry and where to use those.
In this course I will cover all the required knowledge for you to be fluent and confident on Computational Geometry. Following are the topic expected to cover in this course.
Basics of linear algebra including vector and matrix arithmetic and implementation of those operations.
Mathematical representation of basic geometry primitives and implementation.
Computational approach for finding intersections and distance between basic primitives like rectangles, lines, planes etc.
Orientation test on geometric primitives.
Monotone polygon partition.
Plane sweep algorithms.
Convex hull calculations and implementation in both 2D and 3D space.
Overview of simple tree data structures like Binary Search Trees (BST) and Red Black Tree (RBT)
KD Tree implementation and range queries using KDTrees.
Who this course is for:
- Algorithm enthusiasts.
- Competitive Programmers.
- Anyone who seek advanced real-world applications of algorithms
- University students
- Software developers
Software engineer with years of experience in industry with c++ and java programming language. And entrepreneur and founder of intellect. creator of GPU MLIB library which provides GPU optimized parallel implementation of machine learning algorithms. My current project include fashion design framework which allows user to get the live fit on room experience. I am graduate on electrical and information engineering and i currently reading for master in artificial intelligence.