8 ms·

Optimy.io: Optimise mathematical expressions

49 points by malmsteen 9y ago

snissn 9y ago
Who are your target users?
dpcx 9y ago
Getting errors trying to run the default expression. HN Effect already?
ouid 9y ago
Can you please be more specific about the class of expressions you can optimize in the title? It is CLEARLY not any mathematical expression, which means that this is clickbait.
- wenc 9y ago
  I don't want to pour cold water on someone's hobby project if that is what it is. It is quite obvious however that website's description was written by someone who has not actually studied optimization theory or numerical mathematics (my fields of study). I worked with large-scale nonlinear nonconvex problems for over a decade, and there is much more to getting it to work for practical real-world models than this.
d--b 9y ago
I'm sorry but this doesn't make any sense to me. Optimizing mathematical expressions is not a very useful thing to do, and whenever you need it, you'll likely have a derivable function, for which gradient based optimization is much faster. But most importantly, this can't be applied to arbitrary computer functions that you can't have a closed formula for. Also finding a library that does optimization where I can pass a function pointer is usually easy to find in any language. Maybe I'm wrong, but as far as I'm concerned, I'll never use that service.
- adenadel 9y ago
  This is a bit nitpicky, but so you're aware: the term is differentiable, not derivable. You differentiate a function, you don't derive it.
  - thanatropism 9y ago
    Essa é uma questão lingüística, não matemática. Em português há uma diferença diminuta de sentido entre "diferenciável" e "derivável" (na Análise Matemática mesmo), mas não recordo mais qual. O inglês sendo o idioma mutante que é, talvez acabe absorvendo essa diferença de nomenclaturas dessa forma: derivable x differentiable.
    - adenadel 9y ago
      I understand that it is simply a difference of language and that the intent is the same, but in mathematics precision of language is incredibly important. This is a common error in English.
      - fusiongyro 9y ago
        I just want to reflect on the irony of your having no trouble replying to a comment in Portuguese about how it's important to use the right term. This whole thing has brightened my day.
        deleted 9y ago
        [deleted]
    - ORioN63 9y ago
      Translation: That's a linguistic question, not a mathematics one. In Portuguese there's a very small difference between "differentiable" and "derivable" (in the context of Math Analysis), but I don't recall it. English being a mutable language, may end up absorbing the difference between nomenclatures in that way (??): derivable x differentiable.
    - 9y ago
spaceseaman 9y ago
Neat! There seems to be some slight bugs with the input being not read correctly but this is kind of cool.
LolWolf 9y ago
> A (future) extension of this app is the optimisation of functions which cannnot be writted as mathematical expressions (such as a PDE model) by using user-input values of some points of the function to optimise. If this is particularly interesting to you don't hesitate to send a mail. This is an interesting venue, but how would you go about this without solving the PDE model, itself? I work in a similar area of manifold learning, where our objective is a functional of a solution to a PDE (which is, in some sense, a generalization of that idea), but there seems to be no obvious way of optimizing this program without solving for at least the eigenfunctions of the PDE. I'd be curious to hear what the author has in mind. Additionally, I'm also curious as to what algorithms are being used; derivative-free (incl. subgradient-free) methods are usually quite sub-optimal for most classes of problems that I know of, but I'm not sure what the intended audience is, here, so perhaps this would perform quite well in those cases? Anyways, I'd love to test it! But I think HN gave the page the hug-of-death so it's giving me an error...
zzz95 9y ago
Looks good, but it is just another NLP solver in a sea of mature and tested solver implementations. It will be interesting to get the answers for the below questions: - What is the algorithm being used? - How does it compare with other mature optimization engines: e.g. IPOPT, Knitro SNOPT, NPSOL, etc,... - Has the results been published somewhere? Or does there exist a compilation of results on standard benchmarks?

49 points by malmsteen 9y ago

snissn 9y ago

Who are your target users?

dpcx 9y ago

Getting errors trying to run the default expression. HN Effect already?

ouid 9y ago

Can you please be more specific about the class of expressions you can optimize in the title? It is CLEARLY not any mathematical expression, which means that this is clickbait.

wenc 9y ago
I don't want to pour cold water on someone's hobby project if that is what it is. It is quite obvious however that website's description was written by someone who has not actually studied optimization theory or numerical mathematics (my fields of study). I worked with large-scale nonlinear nonconvex problems for over a decade, and there is much more to getting it to work for practical real-world models than this.

d--b 9y ago

I'm sorry but this doesn't make any sense to me. Optimizing mathematical expressions is not a very useful thing to do, and whenever you need it, you'll likely have a derivable function, for which gradient based optimization is much faster. But most importantly, this can't be applied to arbitrary computer functions that you can't have a closed formula for. Also finding a library that does optimization where I can pass a function pointer is usually easy to find in any language. Maybe I'm wrong, but as far as I'm concerned, I'll never use that service.

adenadel 9y ago
This is a bit nitpicky, but so you're aware: the term is differentiable, not derivable. You differentiate a function, you don't derive it.
- thanatropism 9y ago
  Essa é uma questão lingüística, não matemática. Em português há uma diferença diminuta de sentido entre "diferenciável" e "derivável" (na Análise Matemática mesmo), mas não recordo mais qual. O inglês sendo o idioma mutante que é, talvez acabe absorvendo essa diferença de nomenclaturas dessa forma: derivable x differentiable.
  - adenadel 9y ago
    I understand that it is simply a difference of language and that the intent is the same, but in mathematics precision of language is incredibly important. This is a common error in English.
    - fusiongyro 9y ago
      I just want to reflect on the irony of your having no trouble replying to a comment in Portuguese about how it's important to use the right term. This whole thing has brightened my day.
      - deleted 9y ago
        [deleted]
  - ORioN63 9y ago
    Translation: That's a linguistic question, not a mathematics one. In Portuguese there's a very small difference between "differentiable" and "derivable" (in the context of Math Analysis), but I don't recall it. English being a mutable language, may end up absorbing the difference between nomenclatures in that way (??): derivable x differentiable.
  - 9y ago

spaceseaman 9y ago

Neat! There seems to be some slight bugs with the input being not read correctly but this is kind of cool.

LolWolf 9y ago

> A (future) extension of this app is the optimisation of functions which cannnot be writted as mathematical expressions (such as a PDE model) by using user-input values of some points of the function to optimise. If this is particularly interesting to you don't hesitate to send a mail. This is an interesting venue, but how would you go about this without solving the PDE model, itself? I work in a similar area of manifold learning, where our objective is a functional of a solution to a PDE (which is, in some sense, a generalization of that idea), but there seems to be no obvious way of optimizing this program without solving for at least the eigenfunctions of the PDE. I'd be curious to hear what the author has in mind. Additionally, I'm also curious as to what algorithms are being used; derivative-free (incl. subgradient-free) methods are usually quite sub-optimal for most classes of problems that I know of, but I'm not sure what the intended audience is, here, so perhaps this would perform quite well in those cases? Anyways, I'd love to test it! But I think HN gave the page the hug-of-death so it's giving me an error...

zzz95 9y ago

Looks good, but it is just another NLP solver in a sea of mature and tested solver implementations. It will be interesting to get the answers for the below questions: - What is the algorithm being used? - How does it compare with other mature optimization engines: e.g. IPOPT, Knitro SNOPT, NPSOL, etc,... - Has the results been published somewhere? Or does there exist a compilation of results on standard benchmarks?