# Math & Optimizations: Solving Optimization Problems Using Linear Programming

Math    |    Expert
• 12 Videos | 1h 32m 22s
• Includes Assessment
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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

• 1.
Course Overview
• 2.
Understanding the Importance of Optimization
• 3.
Objectives, Decision Variables, and Constraints
• 4.
Optimal Solution and Feasible Solutions
• 5.
Linear Programming
• 6.
Case Study: Happy Pet Food
• 7.
Solving the Problem Formulation Graphically
• 8.
An Overview of the Simplex Method
• 9.
Using the SciPy Library to Minimize Cost
• 10.
Using the SciPy Library to Maximize Profit
• 11.
Solving Linear Programming Problems
• 12.
Course Summary

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