Solving Algorithms for Discrete Optimization from Coursera

Discrete Optimization aims to make good decisions when we have many possibilities to choose from. Its applications are ubiquitous throughout our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions on the use of scarce or expensive resources such as staffing and material resources also allow corporations to improve their profit by millions of dollars. Similar problems also underpin much of our daily lives and are part of determining daily delivery routes for packages, making school timetables, and delivering power to our homes. Despite their fundamental importance, these problems are a nightmare to solve using traditional undergraduate computer science methods.

This course is intended for students who have completed Advanced Modelling for Discrete Optimization. In this course, you will extend your understanding of how to solve challenging discrete optimization problems by learning more about the solving technologies that are used to solve them, and how a high-level model (written in MiniZinc) is transformed into a form that is executable by these underlying solvers. By better understanding the actual solving technology, you will both improve your modeling capabilities, and be able to choose the most appropriate solving technology to use.

Watch the course promotional video here: https://www.youtube.com/watch?v=-EiRsK-Rm08

What's inside

Syllabus

Basic Constraint Programming

This module starts by using an example to illustrate the basic machinery of Constraint Programming solvers, namely constraint propagation and search. While domains represent possibilities for variables, constraints are actively used to reason about domains and can be encoded as domain propagators and bounds propagators. You will learn how a propagation engine handles a set of propagators and coordinates the propagation of constraint information via variable domains. You will also learn basic search, variable and value choices, and how propagation and search can be combined in a seamless and efficient manner. Last but not least, this module describes how to program search in MiniZinc.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Fits well for learners who want to solve challenging discrete optimization problems

Taught by experienced instructors, Prof. Peter James Stuckey and Prof. Jimmy Ho Man Lee

Develops capabilities in modeling, helping learners improve their problem-solving skills

Covers advanced topics like Branch and Bound search, Local Search, and Mixed Integer Programming

Provides a strong foundation in Discrete Optimization, making it suitable for learners who want to pursue further studies or careers in the field

Learners are expected to have completed 'Advanced Modelling for Discrete Optimization' before taking this course

Reviews summary

Solving algorithms for discrete optimization

According to learners, this course provides a deep dive into the solving technologies behind discrete optimization, building directly on the foundational concepts from the prerequisite course. Students found the content, covering Constraint Programming, Mixed Integer Programming, and Local Search, to be challenging but highly rewarding. Many appreciate the focus on understanding how high-level models translate to executable forms by underlying solvers, enhancing their ability to choose appropriate methods. While the course demands effort and assumes prior knowledge, students report gaining a solid understanding and practical skills for tackling complex optimization problems.

Prior knowledge is essential.

"Definitely need to have taken the previous course first."

"If you haven't done the modelling course, this will be very difficult."

"The concepts build directly on the 'Advanced Modelling' course."

Learn to apply algorithms practically.

"The assignments let me see how these algorithms solve real problems."

"Using MiniZinc with different solvers felt very practical."

"I can now approach optimization problems with a better toolkit."

Demands effort but yields significant understanding.

"This course is tough, especially the assignments, but very rewarding."

"While challenging, the payoff in understanding optimization is huge."

"It pushes you to think deeply about algorithmic approaches."

Explore the inner workings of solvers.

"Understanding the internal workings of solvers was invaluable."

"The modules on CP and MIP solvers explained complex concepts clearly."

"I appreciate the focus on how MiniZinc models translate to solver actions."

"Delves deeply into the actual solving technologies used."

Content can be complex and fast-paced.

"The lecture videos were dense and sometimes hard to follow."

"Had to rewatch some lectures multiple times to grasp the concepts."

"Could benefit from more examples in the explanations."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Solving Algorithms for Discrete Optimization with these activities:

Review Discrete Optimization Basics

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Recall the basic concepts and terminologies of Discrete Optimization to reinforce your understanding.

Browse courses on Discrete Optimization

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Reread lecture notes and textbooks from previous courses
Revisit online resources and tutorials
Attempt practice problems and exercises

Organize Course Materials

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Compile resources, notes, and study materials from different sources and organize them systematically for easy access and review.

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Gather lecture notes, slides, and assignments
Review and organize materials by topic
Create a digital or physical study hub
Annotate and highlight key concepts

Explore Constraint Programming Resources

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Enhance your understanding of Constraint Programming (CP) by following online tutorials and resources to strengthen your practical skills.

Browse courses on Constraint Programming

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Identify reputable CP learning platforms and tutorials
Follow step-by-step guides and examples
Experiment with different CP tools and libraries
Apply CP concepts to solve sample problems
Consult online forums and documentation for additional support

Four other activities

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Solve Discrete Optimization Problems

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Sharpen your problem-solving abilities by practicing a variety of Discrete Optimization problems, covering different techniques and domains.

Browse courses on Discrete Optimization

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Access online platforms with problem sets and challenges
Attempt to solve problems independently
Analyze solution strategies and identify optimization techniques
Compare your solutions with others and seek feedback

Develop a Discrete Optimization Model

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Demonstrate your modeling proficiency by designing and implementing a Discrete Optimization model using MiniZinc to solve a real-world problem.

Browse courses on Discrete Optimization

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Identify a suitable problem domain
Formulate the problem as a Discrete Optimization model
Implement the model in MiniZinc
Validate and test the model
Analyze and interpret the results

Attend Industry Conferences and Meetups

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Expand your network and stay up-to-date by engaging with industry professionals at conferences and meetups dedicated to Discrete Optimization and related fields.

Browse courses on Discrete Optimization

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Identify relevant conferences and meetups
Attend sessions and presentations
Network with experts and practitioners
Explore potential collaborations

Contribute to Open-Source Discrete Optimization Projects

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Enhance your practical skills and gain valuable experience by contributing to real-world Discrete Optimization projects on platforms like Github.

Browse courses on Discrete Optimization

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Identify open-source projects in need of assistance
Review codebases and documentation
Identify areas for improvement or contribution
Submit pull requests with bug fixes or enhancements
Engage with project maintainers and community

Career center

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Operations Research Analyst

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