Direct link to paper: http://arxiv.org/abs/1508.04874 I'm not an expert, but my understanding was that this is mostly a theoretical result, similar to finding a new lower bound on matrix multiplication. As far as I know, all BLAS implementations use the O(n^3) matrix multiplication algorithm even though a O(n^2.37) algorithm is known <https://en.wikipedia.org/wiki/Coppersmith%E2%80%93Winograd_algorithm> because the O(n^3) algorithm maps much better onto hardware and has lower constant costs. I don't know as much about it as I do matrix multiplication, but I believe linear programming algorithms are in a similar state, where the simplex algorithm has exponential worst case, interior point algorithms have polynomial worst case, but both methods are used in practice. -Eric On Sat, Oct 24, 2015 at 11:33 AM, Ralf Gommers <ralf.gommers@gmail.com> wrote:

On Sat, Oct 24, 2015 at 6:21 PM, Sturla Molden <sturla.molden@gmail.com> wrote:

...

I am hearing good things about a new local optimization method called "cut-plane optimization". I think we should consider to add this method to scipy.optimize.

http://phys.org/news/2015-10-general-purpose-optimization-algorithm-order-of...

It seems to solve a fairly specific problem. Also, the paper is unreadable (for me). And not including any benchmarks in a paper like this even though it's 111 pages long is odd.....

Ralf

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