Introduction to Optimization Techniques

This course is designed to guide learners through various stages of solving an optimization problem and focuses on providing exposure to building a table of constraints and running the different optimization algorithms to solve the business problems.

  • icon-data 3 Data Cases
  • icon-reading 2 Readings
  • icon-quiz 2 Quizzes
Introduction to Optimization Techniques
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    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended.

  • Asset 1
    Duration: Approximately 4 hours

Course Overview

  • Optimization is one of the types of problems that are often used by analysts for solving real world problems. Some basic optimization techniques that are commonly used are linear programming, mixed integer programming and linear fractional programming.
  • When using this technique, the constrains that apply to a situation are linear in nature and the objective function is to be optimized while fulfilling those constraints.
  • Learn the basics of optimization problems and how to formulate constraints in both linear and mixed-integer programming.
  • Gain practical experience by applying the understanding in several real-world business case scenarios.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Data Aggregation

Fractional Programming

Linear Programming

Mixed-Integer Programming

Optimization Techniques

Constraint Summarization

Associated Learning Tracks

Syllabus

Preparing Summarized Constraints Table, Applying Linear Optimization.

Mixed Integer Programming – Additional constraints of some or all variables to be integers in the linear programming, Fractional Programming – objective function to be a ratio of linear expressions in the linear programming.

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