Cocalc Tutorial 22b Optimization General Ipynb

This notebook uses the Optim.jl package which has general purpose routines for optimization. (Other packages that do similar things are Optimization.jl, NLopt.jl and JuMP.jl) The optimization problems are (for pedagogical reasons) the same as in the other notebook about optimization. This means that the solutions should be very similar and that the contour plots in the other notebook can be used as references. However, the current notebook is focused on methods for solving general optimization problems. In contrast, the other notebook focuses on linear-quadratic problems (mean-variance, least squares, etc), where there are faster algorithms.

finds the x value (in the interval [a,b]) that minimizes fn1(x,0.5). The x->fn1(x,0.5) syntax makes this a function of x only, which is what the optimize() function wants. The output (Sol) contains a lot of information. There was an error while loading. Please reload this page. The methods learned in Chapter 4 of the text for finding extreme values have practical applications in many areas of life.

In this lab, we will use SageMath to help with solving several optimization problems. The following strategy for solving optimization problems is outlined on Page 264 of the text. Read and understand the problem. What is the unknown? What are the given quantities and conditions? Draw a picture.

In most problems it is useful to draw a picture and identify the given and required quantities in the picture. Introduce variables. Asign a symbol for the quantity, let us call it QQQ, that is to be maximized or minimized. Also, select symbols for other unknown quantities. Use suggestive notation whenever possible: AAA for area, hhh for height, rrr for radius, etc. Use this notebook to quickly write your first randomized exercise.

You can refer to the original example as needed while editing (in case you delete the example). Edit the below Code cell to create a function to generate the random data used in your exercise. Use [Ctrl]+[Enter] to see sample output used for your exercise. Edit the following PreTeXt exercise template to write your exercise's statement and answer. There was an error while loading. Please reload this page.

Assume the following situation. From an experiment we have gathered following data: We want to use the data as an input to a simulation. However, as visible, the data is noisy and thus may lead to instability of our simulation. First we will load modules supporting this tutorial. Note that you should install matplotlib first if not already happenend, as only this tutorial needs matplotlib. For usage of ebcpy, you don't need it.

Let's specify the path to our measurement data and load it. If you're familiar with python and DataFrames, you will ask yourself: Why do I need the TimeSeriesData-Class? We implemented this class to combine the powerful pandas.DataFrame class with new functions for an easy usage in the context of Building Energy Systems for three main reasons: Most data in our case is Time-Dependent, therefore functions for easy conversion between seconds (for simulation) and Timestamps (for measurements) is needed There was an error while loading. Please reload this page.

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