Design Optimization

Matlab and Python can both be used to solve optimization problems. In Matlab, functions like fmincon can be used to find the minimum of an objective function. Python has a variety of optimization libraries, such as SciPy, CVXOPT, Pyomo, and Gekko which can be used to solve both linear and nonlinear optimization problems. Additionally, Python also has packages like scikit-optimize for hyperparameter tuning, which can be used to optimize the parameters of an optimizer.

The two packages used throughout this course are the APMonitor Optimization Suite (APM MATLAB or APM Python) and the more recent Python GEKKO package (see GEKKO Documentation).

<b>Python GEKKO<b> <br> <iframe width="560" height="315" src="https://www.youtube.com/embed/bXAkr7MPf4w" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <br><br> <b>APM MATLAB<b> <br> <iframe width="560" height="315" src="https://www.youtube.com/embed/SVOb0yDPJjw" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

(:title Tutorial on MATLAB and Python for Optimization:) (:keywords MATLAB, Python, mathematical modeling, nonlinear, optimization, engineering optimization, interior point, active set, differential, algebraic, modeling language, university course:) (:description It is important to select a flexible and capable platform for complex optimization scenarios. The step-by-step tutorial walks through and optimization problem in engineering with both MATLAB and Python.:)

Tutorial on Optimization with MATLAB and Python

Download MATLAB and Python Tutorial Files (nlp_matlab_python_tutorial.zip)

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