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Network and Discrete Optimization Essentials
Master fundamental concepts in network optimization and discrete optimization, from graph theory to advanced algorithms.
Course Cost
₹ 5,010
Intermediate
Skill Level
5 Weeks
Self-paced lessons
Explore the fascinating world of network and discrete optimization in this comprehensive course. Dive into the mathematical foundations of graphs and networks, and learn to solve complex problems such as the transshipment problem and shortest path algorithms. The course covers essential topics in discrete optimization, introducing you to powerful modeling techniques and exact solution methods. You'll gain practical skills in formulating and solving optimization problems, with a focus on both theoretical understanding and algorithmic implementation. Ideal for students and professionals in mathematics, computer science, and operations research, this course provides a solid foundation for tackling real-world optimization challenges.
English
What you'll learn
Understand and apply the mathematical formalism of graphs and networks
Formulate and solve transshipment (minimum cost flow) problems
Implement and analyze shortest path algorithms for various applications
Specify and model discrete optimization problems
Apply exact methods, including branch and bound and cutting planes, to solve discrete optimization problems
Analyze the computational complexity of network and discrete optimization algorithms
Develop skills in Python programming for implementing optimization algorithms (optional)
Apply optimization techniques to real-world problems in logistics and operations research
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This course provides a comprehensive introduction to network and discrete optimization, covering both theoretical foundations and practical algorithms. The curriculum is structured into five main sections: 1) Networks: introducing the mathematical formalism of graphs and networks; 2) Transshipment: exploring the minimum cost flow problem and its properties; 3) Shortest path: focusing on algorithms to find the shortest path in a network; 4) Discrete optimization: learning how to specify discrete optimization problems; 5) Exact methods for discrete optimization: introducing algorithms to solve discrete optimization problems, including branch and bound and cutting plane methods. Throughout the course, students will learn to apply these concepts to real-world optimization problems, with optional Python programming exercises to implement the algorithms discussed. The course emphasizes both mathematical rigor and practical problem-solving skills, preparing students for advanced study or application of optimization techniques in various fields.
Fee Structure
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Faculties
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Frequently asked Questions
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