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孔令傑 (Ling-Chieh Kung)

Operations Research (OR) is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc.

The series of courses consists of three parts, we focus on deterministic optimization techniques, which is a major part of the field of OR.

As the second part of the series, we study some efficient algorithms for solving linear programs, integer programs, and nonlinear programs.

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Operations Research (OR) is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc.

The series of courses consists of three parts, we focus on deterministic optimization techniques, which is a major part of the field of OR.

As the second part of the series, we study some efficient algorithms for solving linear programs, integer programs, and nonlinear programs.

We also introduce the basic computer implementation of solving different programs, integer programs, and nonlinear programs and thus an example of algorithm application will be discussed.

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What's inside

Syllabus

Course Overview
In the first lecture, we briefly introduce the course and give a quick review about some basic knowledge of linear algebra, including Gaussian elimination, Gauss-Jordan elimination, and definition of linear independence.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches tools and techniques that are indispensable in business, management, economics, and technology
Provides a comprehensive overview of deterministic optimization techniques within operations research
Involves computer implementation of algorithms, providing practical experience
Involves real-world case studies, connecting theories to practical applications
Introduces essential optimization algorithms like the Simplex Method, Branch-and-Bound, and Gradient Descent
Covers nonlinear programming, extending the scope of optimization techniques

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Reviews summary

Easy-to-understand, practical optimization algorithms course

Learners say this course provides clear and comprehensive coverage of optimization algorithms, with engaging assignments and helpful real-world examples. The lectures are easy to understand, even for beginners. Some learners have found the exams a bit vague and abstract, and the homework assignments could be improved with more detailed instructions. Overall, students appreciate the course's practical approach and find it helpful for preparing for exams and their careers.
Suitable for learners new to optimization algorithms.
"good for beginners!!"
"I like it a lot."
"very practical and omits details that are not required to understand the topic."
Straightforward explanations make concepts easy to grasp.
"Very clear explanations"
"The videos are very easy to understand"
"I like it a lot."
Emphasis on practical applications with real-world examples.
"It's a course that teaches you to solve the model you've built."
"Very clear explanations and great coverage from main points of view -- mathematical, technical and even business"
"I am learning a lot."
Homework assignments could use more detailed instructions.
"The ​course is good, but homework assignments could be made clear."
"For heuristics algorithms hints, it is better to hint the type of algorithm (greedy, brute-force, dynamic, etc) and let the students find their solutions"
Some exam questions could be more specific.
"Some questions in tests were a little vague, abstract."
"Last quiz was disproportionately difficult to the content of the course."

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 Operations Research (2): Optimization Algorithms with these activities:
OR Industry Meetup
Networking with professionals in the field can provide insights into career opportunities and industry trends.
Show steps
  • Research and identify an industry meetup related to OR.
  • Prepare an elevator pitch and some questions to ask other attendees.
  • Attend the meetup and actively engage in conversations.
Linear Algebra Practice
Reviewing key concepts from linear algebra helps you to build a strong foundation for operations research.
Browse courses on Linear Algebra
Show steps
  • Solve systems of linear equations using Gaussian elimination or Gauss-Jordan elimination.
  • Calculate nullspaces and column spaces of matrices
  • Determine if a set of vectors is linearly independent or dependent.
Introduction to Operations Research
This book can offer a comprehensive overview of Operations Research, covering fundamental concepts and techniques that are essential for the course.
Show steps
  • Read the chapters on linear programming, integer programming, and nonlinear programming.
  • Solve the practice problems at the end of each chapter.
Three other activities
Expand to see all activities and additional details
Show all six activities
Gradient Descent Tutorial
This tutorial can provide a clear and structured approach to understanding the concepts behind gradient descent.
Browse courses on Gradient Descent
Show steps
  • Review the basics of derivatives and partial derivatives.
  • Learn how to calculate the gradient of a function.
  • Apply gradient descent to optimize a simple function.
Operations Research Workshop
Attending a workshop can provide valuable hands-on experience and allow you to learn from experts in the field.
Browse courses on Operations Research
Show steps
  • Research and identify a relevant workshop.
  • Register for the workshop and prepare any necessary materials.
  • Attend the workshop and actively participate in discussions and exercises.
Develop a Simulation Model
Building a simulation model allows you to test and evaluate different scenarios and gain insights into complex systems.
Browse courses on Simulation
Show steps
  • Define the system or process to be simulated.
  • Collect and analyze relevant data.
  • Develop a simulation model using an appropriate software tool.
  • Run the simulation and analyze the results.

Career center

Learners who complete Operations Research (2): Optimization Algorithms will develop knowledge and skills that may be useful to these careers:
Quantitative Analyst
Quantitative Analysts (QAs) create and apply mathematical and statistical models to solve problems for financial institutions and other companies. They use their knowledge of mathematics, statistics, and computer programming to analyze data, identify trends, and make predictions. The Operations Research (2): Optimization Algorithms course can help QAs build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for QAs who need to develop and implement optimization models that solve real-world problems facing companies.
Operations Research Analyst
Operations Research Analysts apply mathematical and analytical techniques to improve the efficiency and effectiveness of operations within organizations. They use their knowledge of mathematics, statistics, and computer programming to analyze data, identify problems, and develop solutions. The Operations Research (2): Optimization Algorithms course can help Operations Research Analysts build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Operations Research Analysts who need to develop and implement optimization models that solve real-world problems facing organizations.
Management Consultant
Management Consultants help organizations improve their performance by providing advice on strategy, operations, and technology. They use their knowledge of business, mathematics, and computer programming to analyze data, identify problems, and develop solutions. The Operations Research (2): Optimization Algorithms course can help Management Consultants build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Management Consultants who need to develop and implement optimization models that solve real-world problems facing organizations.
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer programming to analyze data, identify trends, and make predictions. They work in a variety of industries, including finance, healthcare, and retail. The Operations Research (2): Optimization Algorithms course can help Data Scientists build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Data Scientists who need to develop and implement optimization models that solve real-world problems facing organizations.
Financial Analyst
Financial Analysts use their knowledge of mathematics, statistics, and computer programming to analyze financial data and make investment recommendations. They work in a variety of industries, including banking, investment management, and insurance. The Operations Research (2): Optimization Algorithms course can help Financial Analysts build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Financial Analysts who need to develop and implement optimization models that solve real-world problems facing organizations.
Actuary
Actuaries use their knowledge of mathematics, statistics, and computer programming to assess and manage risk. They work in a variety of industries, including insurance, pension funds, and healthcare. The Operations Research (2): Optimization Algorithms course can help Actuaries build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Actuaries who need to develop and implement optimization models that solve real-world problems facing organizations.
Industrial Engineer
Industrial Engineers use their knowledge of mathematics, statistics, and computer programming to improve the efficiency and effectiveness of industrial processes. They work in a variety of industries, including manufacturing, transportation, and healthcare. The Operations Research (2): Optimization Algorithms course can help Industrial Engineers build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Industrial Engineers who need to develop and implement optimization models that solve real-world problems facing organizations.
Software Engineer
Software Engineers use their knowledge of mathematics, statistics, and computer programming to design, develop, and implement software applications. They work in a variety of industries, including technology, finance, and healthcare. The Operations Research (2): Optimization Algorithms course can help Software Engineers build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Software Engineers who need to develop and implement optimization models that solve real-world problems facing organizations.
Systems Analyst
Systems Analysts use their knowledge of mathematics, statistics, and computer programming to analyze and design computer systems. They work in a variety of industries, including technology, finance, and healthcare. The Operations Research (2): Optimization Algorithms course can help Systems Analysts build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Systems Analysts who need to develop and implement optimization models that solve real-world problems facing organizations.
Operations Manager
Operations Managers use their knowledge of mathematics, statistics, and computer programming to manage the operations of organizations. They work in a variety of industries, including manufacturing, transportation, and healthcare. The Operations Research (2): Optimization Algorithms course can help Operations Managers build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Operations Managers who need to develop and implement optimization models that solve real-world problems facing organizations.
Business Analyst
Business Analysts use their knowledge of mathematics, statistics, and computer programming to analyze business processes and identify opportunities for improvement. They work in a variety of industries, including technology, finance, and healthcare. The Operations Research (2): Optimization Algorithms course can help Business Analysts build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Business Analysts who need to develop and implement optimization models that solve real-world problems facing organizations.
Statistician
Statisticians use their knowledge of mathematics, statistics, and computer programming to collect, analyze, and interpret data. They work in a variety of industries, including technology, finance, and healthcare. The Operations Research (2): Optimization Algorithms course can help Statisticians build a foundation in the mathematical and algorithmic techniques used to solve optimization problems. This course covers topics such as linear programming, integer programming, and nonlinear programming, which are all essential for Statisticians who need to develop and implement optimization models that solve real-world problems facing organizations.
Economist
Economists use their knowledge of mathematics, statistics, and computer programming to analyze economic data and make predictions about the economy. They work in a variety of industries, including government, academia, and business. The Operations Research (2): Optimization Algorithms course may be helpful for Economists who need to develop and implement optimization models that solve real-world problems facing organizations.
Market Researcher
Market Researchers use their knowledge of mathematics, statistics, and computer programming to collect and analyze data about consumer behavior. They work in a variety of industries, including technology, finance, and healthcare. The Operations Research (2): Optimization Algorithms course may be helpful for Market Researchers who need to develop and implement optimization models that solve real-world problems facing organizations.
Financial Planner
Financial Planners use their knowledge of mathematics, statistics, and computer programming to help individuals and families plan for their financial future. They work in a variety of industries, including banking, investment management, and insurance. The Operations Research (2): Optimization Algorithms course may be helpful for Financial Planners who need to develop and implement optimization models that solve real-world problems facing organizations.

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Operations Research (2): Optimization Algorithms.
Provides a more in-depth look at linear programming, including more advanced topics such as duality and sensitivity analysis. It good resource for students who want to learn more about the theory of linear programming.
Provides a comprehensive overview of integer programming, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of integer programming.
Provides a comprehensive overview of nonlinear programming, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of nonlinear programming.
Provides a comprehensive overview of optimization algorithms, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of optimization algorithms.
Provides a comprehensive overview of discrete optimization, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of discrete optimization.
Provides a comprehensive overview of combinatorial optimization, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of combinatorial optimization.
Provides a comprehensive overview of convex optimization, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of convex optimization.
Provides a comprehensive overview of stochastic programming, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of stochastic programming.
Provides a comprehensive overview of robust optimization, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of robust optimization.
Provides a comprehensive overview of calculus, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of calculus.
Provides a comprehensive overview of probability and statistics, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of probability and statistics.
Provides a comprehensive overview of linear algebra, including both theoretical and practical aspects. It good resource for students who want to learn more about the theory and practice of linear algebra.

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