altavir

    altavir

    1 year ago
    @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.
    Optimizers have too many configuration handles, so there probably will be additional intermediate configuration object.
    b

    bjonnh

    1 year ago
    This is cool
    altavir

    altavir

    1 year ago
    A 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.
    b

    bjonnh

    1 year ago
    Pretty busy this week, but I want to give it a try
    altavir

    altavir

    1 year ago
    I'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.