mlubin

6 ms·

1.
JuMP 1.0.0 Is Released(jump.dev)
28 points by mlubin 4y ago|2 comments
2.
by mlubin 6y ago
> I remember when using download statistics was enough. No download statistics are currently available for Julia packages. That's essentially the issue that the Pkg.jl telemetry is trying to address.
3.
by mlubin 11y ago
Very impressive work, here's the arxiv link: http://arxiv.org/abs/1508.04874
4.
by mlubin 11y ago
If you're interested in model generation time, have a look at JuMP ( https://github.com/JuliaOpt/JuMP.jl ). More discussions on the speed of modeling languages at http://arxiv.org/abs/1312.1431
5.
JuliaCon 2015 Preview(julialang.org)
72 points by mlubin 11y ago|18 comments
6.
by mlubin 11y ago
The only PhD defense I've seen with standing room only. Congrats!
7.
JuliaCon, June 24-28 at MIT(juliacon.org)
3 points by mlubin 11y ago|0 comments
8.
Computing Delacorte Numbers with Julia(software.intel.com)
7 points by mlubin 12y ago|0 comments
9.
by mlubin 12y ago
Some light reading for my next long flight.
10.
by mlubin 12y ago
I really would like to be able to see a proof of how Mathematica calculates a limit, for example. Doesn't need to be human friendly, just verifiable.
11.
by mlubin 12y ago
This is an impressive job. I'm curious why the arxiv paper uses Julia's benchmark suite but doesn't make any other mention of Julia in the discussion, given that it's quite relevant to this work.
12.
by mlubin 12y ago
The exchange would be based on the counterparty protocol, which allows anyone with a BTC address to issue and back assets in a decentralized manner. We'll have to see how this decentralization leaks into what it means to be a stock exc
13.
Sudoku, Linear Optimization, and the Ten Cent Diet(googledevelopers.blogspot.com)
2 points by mlubin 12y ago|0 comments
14.
by mlubin 12y ago
To follow up on this, a lot of the overhead from reverse-mode AD can be avoided by flattening out the expression graphs and compiling specialized functions (at runtime). A number of the JuliaDiff packages support this approach. This is not
15.
SIMD Vectorization in Julia(software.intel.com)
126 points by mlubin 12y ago|11 comments
16.
Announcing Julia 0.3.0 final(groups.google.com)
6 points by mlubin 12y ago|0 comments
17.
by mlubin 12y ago
Should the IPLANG "compiler" try to detect multiple solutions?
18.
by mlubin 12y ago
A couple of projects have looked at solving the trust issue by implementing a distributed exchange without counterparty risk on top of the bitcoin blockchain: https://www.counterparty.co/ http://www.mastercoin.or
19.
by mlubin 13y ago
"Hack provides instantaneous type checking via a local server that watches the filesystem." What does this mean?
20.
by mlubin 13y ago
Very cool. Missing LGPL v3. I've been looking for a clear explanation of how it differs from LGPL v2.1.
21.
Julia in GSOC 2014(julialang.org)
7 points by mlubin 13y ago|0 comments
22.
by mlubin 13y ago
koding.com is another way to get an interactive REPL online: https://koding.com/Julia
23.
by mlubin 13y ago
For anyone interested in using Julia for numerical optimization/mathematical programming, see http://juliaopt.org/ . We have pure-Julia implementations of standard unconstrained methods [1] as well as a domain-specific
24.
by mlubin 13y ago
That's a good tip in general, but it's mostly aimed at addressing type instability. For example, if you have x = 1 f(x) x = "foobar" f(x) then each call to f will be optimized for the current type of x. O
25.
by mlubin 13y ago
If the goal is to get performance without giving up on Python's existing libraries, why not use Steven Johnson's PyCall ( https://github.com/stevengj/PyCall.jl ) package for Julia?
26.
by mlubin 14y ago
I've added a bit of an explanation as to why there's no NumPy version.