Solving Algorithms for Discrete Optimization
Master advanced discrete optimization techniques. Learn constraint programming, mixed integer programming, and local search methods in 22 hours.
Course Cost
₹ 2,699
Intermediate
Skill Level
19 Hours
Self-paced lessons
Dive deep into solving challenging discrete optimization problems with this intermediate-level course. Extend your understanding of solving technologies used in discrete optimization, including constraint programming, mixed integer programming, and local search methods. Learn how high-level MiniZinc models are transformed for execution by underlying solvers. Improve your modeling capabilities and choose appropriate solving technologies for various optimization scenarios. This course builds on the Advanced Modelling for Discrete Optimization course, offering practical insights into solver mechanics and advanced search strategies.
What you'll learn
Understand the basic machinery of Constraint Programming solvers
Master advanced search strategies for optimization problems
Explore the inner workings of global constraints like alldifferent and cumulative
Learn linear programming and the Simplex algorithm for continuous optimization
Understand Mixed Integer Programming and cutting plane methods
Explore local search methods and techniques for escaping local minima
Gain practical experience with MiniZinc for modeling and solving optimization problems
Skills you'll gain
This course includes:
5 Hours PreRecorded video
4 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Closed caption

Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.





There are 4 modules in this course
This course delves into the solving technologies used in discrete optimization, focusing on constraint programming, mixed integer programming, and local search methods. Students will learn about constraint propagation, search strategies, and the inner workings of global constraints. The curriculum covers linear programming, the Simplex algorithm, and advanced techniques like Gomory Cuts and Branch and Cut methods. Participants will also explore local search methods, including simulated annealing, tabu search, and large neighborhood search. Throughout the course, students will gain practical experience by applying these concepts to real-world optimization problems using MiniZinc.
Basic Constraint Programming
Module 1 · 5 Hours to complete
Advanced Constraint Programming
Module 2 · 5 Hours to complete
Mixed Integer Programming
Module 3 · 4 Hours to complete
Local Search
Module 4 · 5 Hours to complete
Fee Structure
Payment options
Financial Aid
Reviews
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
Frequently asked Questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.






