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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 2381	yes	Coursera

Course Name	Fees in INR (Per Month)
Discrete Optimization	Rs. 2,381

Students can join the course for free but will have to pay for the full specialization, resources, and the course completion certificate at the end.

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

Programming skills

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

Product Development ManagerScientistResearch AssociateProject ManagerOperations Manager

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

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

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

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

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

Videos

Scheduling - jobshop, disjunctive global constraint
Vehicle Routing

Videos

Large Neighborhood Search - asymmetric TSP with time windows
Column Generation - branch and price, cutting stock

Reading

End of course survey

Instructors

University of Melbourne, Parkville Frequently Asked Questions (FAQ's)

1: What prior knowledge is needed for this course?

Students must have good programming knowledge and experience as well as a basic knowledge of the computer language Python for this course. This is an intermediate level course so students must have this knowledge in order to grasp the concepts well.

2: What does the ‘audit mode’ mean?

In audit mode, students can only read and view the course material. If you wish to get a certificate along with other resources like graded assignments and access to whole course content, then you must get the specialized version or certificate experience.

3: How will I get practical knowledge in this course?

Students are regularly given assignments that test their knowledge of everything they learn in the course and allows them to use their skills to solve practical problems. These assignments are an important part of the course and challenge the students on an intellectual level.

4: When will I receive the electronic certificate?

To get the shareable E-certificate, students must finish the complete course and then the electronic certificate will be added to your accomplishments page. First applicants have to download and later it can be printed and attached with Resume/CV or shared virtually on online platforms such as your LinkedIn account.

5: What method is used to teach in this course?

The Coursera Discrete Optimization course is 100% online for the convenience of the students and lecturers are pre-recorded by the trainers in the form of videos. Quizzes and assignments are given to students to provide hands-on experience, and extra readings are given to enhance knowledge.

6: Is any financial aid available from Coursera for this programme?

Yes, Coursera provides financial aid for this course which can be availed by students who cannot afford the fee. You just have to click on the financial aid option and complete an application to be eligible.