<@UAA7Q4ZCH> I was playing with new symbol API and...
# mathematicsa
altavir
10/24/2020, 5:30 PM@bjonnh I was playing with new symbol API and I managed to befriend it with autodiff and commons math optimization. It is still very preliminary since I did not manage to make commons-math optimization safe enough, but the example from this test works fine both with simplex and gradient descent.
altavir
10/24/2020, 5:32 PMOptimizers have too many configuration handles, so there probably will be additional intermediate configuration object.
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bjonnh
10/24/2020, 11:38 PMThis is cool
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altavir
10/25/2020, 4:48 PMA cleaned-up API: https://github.com/mipt-npm/kmath/blob/feature/optimization/kmath-commons/src/test/kotlin/kscience/kmath/commons/optimization/OptimizeTest.kt. It is not final on the inside but close to final on the outside. Again, comments and criticism would be appreciated. The final step will be to add X-Y data fitting.
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bjonnh
10/26/2020, 2:58 PMPretty busy this week, but I want to give it a try
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altavir
10/26/2020, 3:00 PMI've finished an example with x-y fit. It looks really nice since it combines both autodiff and gradient descent in one place with full customization, but the design is not final. There are still things I find non-intuitive. The example is the last test in the reference above.
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