This course introduces students to the fundamental concepts and techniques of optimization theory. It covers linear programming, including problem formulation, graphical and algebraic solutions, and the simplex algorithm. Topics include duality, sensitivity analysis, transportation problems, integer programming, and unconstrained and constrained optimization in R^n. Students will learn to apply these methods to solve real-world problems in various fields.
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Everything you need to know about this course
Key areas covered in this course
No specific requirements needed
This course is designed to be accessible to all students. You can start immediately without any prior knowledge or specific preparation.
How your progress will be evaluated (3 methods)
Comprehensive evaluation of course material understanding
Comprehensive evaluation of course material understanding
Comprehensive evaluation of course material understanding
Explore the career paths this course opens up for you
Apply your skills in this growing field
Apply your skills in this growing field
Apply your skills in this growing field
Apply your skills in this growing field
Apply your skills in this growing field
Real-world sectors where you can apply your knowledge
Expert tips to help you succeed in this course
Review all key definitions and theorems related to linear programming.
Practice formulating LP problems from various scenarios.
Master the steps of the simplex algorithm and its variations.
Understand the relationship between primal and dual problems.
Practice solving transportation and integer programming problems.
Focus on understanding the assumptions and limitations of each method.
Create concept maps linking modules and units
Review all TMAs and assignments