An Introduction to Optimization for STEM Students
Optimization methods programmed in Fortran90 and Python. Downloadable notes and codes.
Ratings 4.57 / 5.00
What You Will Learn!
- Basic Techniques in Engineering Optimization
- Newtons and Secant methods for one dimensional unconstrained problems.
- Golden search bracketed method for one-dimensional unconstrained problems.
- Univariate search for multi-dimensional unconstrained problems.
- Steepest Ascent Method for multi-dimensional unconstrained problems.
- Newton’s Method for a multi-dimensional unconstrained problem.
- Lagrange multiplier method for multi-dimensional equality constraint problems.
- Lagrange multiplier method for multi-dimensional inequality constraint problems.
Description
This course provides a basic introduction to optimization methods for science and engineering students which is often taught as part of an undergraduate-level numerical methods class. The material covered here is at that level, and includes:
· Newton and Secant methods for one dimensional unconstrained problems.
· Golden search bracketed method for one-dimensional unconstrained problems.
· Univariate search for multi-dimensional unconstrained problems.
· Steepest Ascent Method for multi-dimensional unconstrained problems.
· Newton’s Method for a multi-dimensional unconstrained problem.
· Lagrange multiplier method for multi-dimensional equality constraint problems.
· Lagrange multiplier method for multi-dimensional inequality constraint problems.
· Example problems using the above methods.
Course notes are available for download. Computer codes used to solve these problems, written in both Fortran95 and Python, are also available for download and may be easily modified for your own use.
The material presented is suitable for students in a sophomore or junior level science, technology, engineering and/or mathematics numerical methods class. A background in calculus is necessary, as is the ability to program in a computer language such as Fortran90, C, C++, Python, MatLab, etc.
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Who Should Attend!
- Engineering students at the sophomore/junior level and others interested in learning basic optimization techniques who have the necessary background.
