Related to Transportation theory
https://en.wikipedia.org/wiki/Transportation_theory_(mathematics) https://en.wikipedia.org/wiki/Wasserstein_metric
Something like factorio, where you want to optimise logistics. Found it here, https://github.com/matthieuheitz/WassersteinDictionaryLearning which I came across while looking for ways to visualise npy files.
In mathematics, the Wasserstein or Kantorovich–Rubinstein metric or distance is a distance function defined between probability distributions on a given metric space {\displaystyle M}
Intuitively, if each distribution is viewed as a unit amount of “dirt” piled on {M}, the metric is the minimum “cost” of turning one pile into the other, which is assumed to be the amount of dirt that needs to be moved times the mean distance it has to be moved. Because of this analogy, the metric is known in computer science as the earth mover’s distance.
https://arxiv.org/pdf/1708.01955.pdf is full of statistical optimisation jargon, related to this https://en.wikipedia.org/wiki/Sparse_dictionary_learning which mentions Stochastic gradient descent as a type. So it’s like sampling something and generalising a function.
“Sparse coding is a representation learning method which aims at finding a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms and they compose a dictionary. “
https://en.wikipedia.org/wiki/Duality_(optimization) In mathematical optimization theory, duality or the duality principle is the principle that optimization problems may be viewed from either of two perspectives, the primal problem or the dual problem. The solution to the dual problem provides a lower bound to the solution of the primal (minimization) problem.[1] However in general the optimal values of the primal and dual problems need not be equal. Their difference is called the duality gap. For convex optimization problems, the duality gap is zero under a constraint qualification condition.
For the npy files I came across his https://github.com/matthieuheitz/npy_viewer which had a couple cool programs.
