Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Optimization technology is used in many industries to make problem-solving easier. The Discrete Optimization course by Coursera focuses on solving complex problems using optimization algorithms and concepts, including local search, constraint programming, and mixed-integer programming. The course gives an introduction to the fundamental concepts of discrete optimization and its uses in the real world.
The Coursera Discrete Optimization course explores concepts like mixed-integer programming, constraint programming and local search, starting from their foundations to their uses and application for solving practical problems, which includes scheduling, resource allocation, vehicle routing and supply-chain optimization.
Furthermore, the Coursera Discrete Optimization course is an online, self-paced learning course offered by the University of Melbourne to help students understand the process of solving complex problems through discrete optimization. Coursera also offers a shareable electronic certificate at the end of the course, which can be printed and used along with the student’s resume, and also shared on professional platforms such as LinkedIn.
The Highlights
- Self-Paced online course
- Flexible deadlines
- Subtitles in English, French, Russian, Spanish, and Portuguese (Brazilian)
- Intermediate-level course
- Graded quizzes and assignments
- Shareable Certificate
- Completion in approximately 65 hours
- Free enrolment
Programme Offerings
- online learning
- Flexible Deadlines
- Shareable Certificate
- free trails
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 2480 | yes | Coursera |
Discrete Optimization Fees Structure :
| Course Name | Fees |
| Discrete Optimization (with certificate) | Rs. 2,480 |
Eligibility Criteria
As the Coursera Discrete Optimization course is an intermediate level course, students must have good programming skills along with knowledge of linear algebra and some algorithms. Knowledge of computer languages such as Python is also important.
What you will learn
After completing the Coursera Discrete Optimization online course, you will learn about:
- Knowledge of paradigms like constraint programming as well as Branch and Bound that is used in practical problem-solving.
- Exposure to some fundamental algorithms that are used in solving complex problems.
- Ability to use constraint programming, local search, and mixed-integer programming for real-world problems in fields such as vehicle routing, supply-chain optimization, and resource allocation.
- Practical skills to solve complex problems using discrete optimization algorithms that are taught through challenging yet helpful assignments.
Who it is for
Admission Details
To apply to the Discrete Optimization online course by Coursera, you should follow these steps:
- Visit the official website of Coursera
- Find the ‘Discrete Optimization online course’ on the website.
- Choose the ‘Enroll for FREE’ option.
- A dialogue box will appear. Sign up using your Google or Apple id to start the course.
- If you already have an account, use those details to log in and proceed.
Application Details
To join the Coursera Discrete Optimization course, use your email id and password to sign up on the website using your Email ID and password and then you will be eligible for joining the course.
The Syllabus
Videos
- Course Promo
- Course Motivation - Indiana Jones, challenges, applications
- Course Introduction - philosophy, design, grading rubric
- Assignments Introduction & Any Integer
Readings
- Start of Course Survey
- Course Syllabus
Programming Assignment
- Any Integer
Videos
- Knapsack 1 - intuition
- Knapsack 2 - greedy algorithms
- Knapsack 3 - modelling
- Knapsack 4 - dynamic programming
- Knapsack 5 - relaxation, branch and bound
- Knapsack 6 - search strategies, depth-first, best first, least discrepancy
- Assignments Getting Started
- Knapsack & External Solver
- Exploring the Material - open course design, optimization landscape, picking your adventure
Programming Assignment
- Knapsack
Videos
- CP 1 -intuition, computational paradigm, map colouring, n-queens
- CP 2 - propagation, arithmetic constraints, send+more=money
- CP 3 - reification, element constraint, magic series, stable marriage
- CP 4 - global constraint intuition, table constraint, sudoku
- CP 5 - symmetry breaking, BIBD, scene allocation
- CP 6 - redundant constraints, magic series, market split
- CP 7 - car sequencing, dual modelling
- CP 8 - global constraints in detail, knapsack, all different
- CP 9 - search, first-fail, Euler knight, ESDD
- CP 10 - value/variable labelling, domain splitting, symmetry breaking in search
- Graph Coloring
- Optimization Tools
- Set Cover
Reading
- Optimization Tools
Programming Assignments
- Set Cover
- Graph Coloring
Videos
- LS1 - intuition, n-queens
- LS 2 - swap neighbourhood, car sequencing, magic square
- LS 3 - optimization, warehouse location, travelling salesman, 2-opt, k-opt
- LS 4 - optimality vs feasibility, graph colouring
- LS 5 - complex neighbourhoods, sports scheduling
- LS 6 - escaping local minima, connectivity
- LS 7 - formalization, heuristics, meta-heuristics introduction
- LS 8 - iterated location search, metropolis heuristic, simulated annealing, tabu search intuition
- LS 9 - tabu search formalized, aspiration, car sequencing, n-queens
- Travelling Salesman
Programming Assignment
- Traveling Salesman
Videos
- LP 1 - intuition, convexity, geometric view
- LP 2 - algebraic view, the naive algorithm
- LP 3 - the simplex algorithm
- LP 4 - matrix notation, the tableau
- LP 5 - duality derivation
- LP 6 - duality interpretation and uses
Videos
- MIP1 - intuition, relaxation, branch and bound, knapsack, warehouse location
- MIP 2 - modelling, big-M, warehouse location, graph colouring
- MIP 3 - cutting planes, Gomory cuts
- MIP 4 - convex hull, polyhedral cuts, warehouse location, node packing, graph colouring
- MIP 5 - cover cuts, branch and cut, seven bridges, travelling salesman
- Facility Location
Programming Assignment
- Facility Location
Videos
- Scheduling - jobshop, disjunctive global constraint
- Vehicle Routing
Programming Assignment
- Vehicle Routing
Videos
- Large Neighborhood Search - asymmetric TSP with time windows
- Column Generation - branch and price, cutting stock
Reading
- End of course survey