fast-cma-es VS jax

essentially the accumulated value of the portfolio after 50 years not clear to me how this can be linear - looks quite "exponential" without knowing the details. Can you exploit the "has to be greater than 0" condition to simplify the constraint into a linear one? "because at each time step there will be a decision" probably means the answer is "no". But don't overestimate the complexity of nonlinear optimizaiton (see for instance https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/CryptoTrading.adoc ), most of the complexity is hidden in the algorithm itself not visible for the user.

What about combining a fast numerical integrator like https://github.com/esa/torchquad or https://github.com/AnyarInc/Ascent with a fast parallel CMA-ES implementation like https://github.com/dietmarwo/fast-cma-es/blob/master/fcmaes/cmaescpp.py ? A numerical integrator allows you to implement variable control and a fast non-derivative optimizer can solve any related optimization problem.

A new tutorial how to apply QD-optimization to expensive simulations: https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/Diversity.adoc .

There is a new implementation of Python CVT MAP-Elites + CMA-ES available. It is presented at https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/MapElites.adoc applying it to ESAs very hard Cassini2 space mission planning optimization benchmark.

I tried to answer these questions in EvoJax.adoc

Here https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/Quant.adoc is a new tutorial how to apply optimization in the context of simulated quantum algorithms. It is based on https://qiskit.org/textbook/ch-applications/vqe-molecules.html#Example-with-a-Single-Qubit-Variational-Form but provides more reliable methods utilizing parallelism. This makes not much sense (yet) when the backend is a real quantum computer, but most simulators scale bad when using multi-threading or an GPU. So it is better to switch parallelism off for the simulation and utilize the better scaling parallel optmization provides, specially if a modern many-core CPU is available.

https://github.com/dietmarwo/fast-cma-es/blob/master/examples/subset.py implements the problem using parallel continuous optimization collecting different optimal solutions. Not much faster than GLPK_MI, but utilizing modern many-core CPUs when you are looking for a list of alternative solutions. Increase the number of retrys when you want more solutions.

Do you mind if I apply the generic optimization approach shown here: https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/OneForAll.adoc to this problem to compare results? I see you collected a huge number of benchmark instances. Are there solutions proven to be optimal availabe for these?

using Python you may get some inspiration here: https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/Tutorials.adoc

The dual numbers exist just as surely as the real numbers and have been used well over 100 years

https://en.m.wikipedia.org/wiki/Dual_number

Pytorch has had them for many years.

https://pytorch.org/docs/stable/generated/torch.autograd.for...

JAX implements them and uses them exactly as stated in this thread.

https://github.com/google/jax/discussions/10157#discussionco...

As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.

On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.

Source: https://github.com/google/jax/discussions/5199#discussioncom...

The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.

I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html

There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...

I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.

But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566

>

Are they even comparing apples to apples to claim that they see these improvements over NumPy?

> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.

NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.

[1] https://github.com/google/jax

Actually that never changed. The README has always had an example of differentiating through native Python control flow:

https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...

The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!

For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...

2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)

Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:

3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)

4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...

Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.

Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1

The simplest example might be {0}^* in which case

0: “” // because we use *

Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!