Math & Optimizations: Solving Optimization Problems Using Linear Programming

Math    |    Expert
  • 12 videos | 1h 27m 22s
  • Includes Assessment
  • Earns a Badge
Rating 4.2 of 101 users Rating 4.2 of 101 users (101)
Mathematical optimization models allow us to represent our objectives, decision variables, and constraints in mathematical terms, and solving these models gives us the optimal solution to our problems. Linear programming is an optimization model that can be used when our objective function and constraints can be represented using linear terms. Use this course to learn how decision-making can be represented using mathematical optimization models. Begin by examining how optimization problems can be formulated using objective functions, decision variables, and constraints. You'll then recognize how to find an optimal solution to a problem from amongst feasible solutions through a case study. This course will also help you investigate the pros and cons of the assumptions made by linear programming and the steps involved in solving linear programming problems graphically as well as by using the Simplex method. When you are done with this course, you will have the skills and knowledge to apply linear programming to solve optimization problems.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Recognize the use of optimization to make decisions involving trade-offs
    Model problems using objectives, decision variables, and constraints
    Determine the optimal solution from feasible solutions
    List the assumptions and benefits of linear optimization models
    Formulate the linear programming model for happy pet food
  • Solve the linear programming problem graphically
    List the steps in the simplex method to solve linear programming problems
    Solve minimization problems using the scipy library
    Solve maximization problems using the scipy library
    Solve linear programming problems using the pulp library
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 19s
  • 5m 5s
  • Locked
    3.  Objectives, Decision Variables, and Constraints
    10m 54s
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    4.  Optimal Solution and Feasible Solutions
    6m 28s
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    5.  Linear Programming
    7m 1s
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    6.  Case Study: Happy Pet Food
    10m 34s
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    7.  Solving the Problem Formulation Graphically
    5m 41s
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    8.  An Overview of the Simplex Method
    4m 59s
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    9.  Using the SciPy Library to Minimize Cost
    13m 24s
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    10.  Using the SciPy Library to Maximize Profit
    9m 12s
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    11.  Solving Linear Programming Problems
    9m 59s
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    12.  Course Summary
    1m 44s

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