191–200 of 210 posts
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One of my favourite courses in grad school was approximation algorithms and it involved reductions to LP. Lots of fun, can recommend.
Do you have a link to some materials to help get me started? I did an optimization/ILP MOOC once and that was indeed a lot of fun.
In particular, seems like lectures 9-11 have LP content.
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it's not. it's np-hard. the easiest proof is that the best known algorithm is greater than O(2^N)
0/1 ILP is NP-hard and the trivial algorithm takes O(2^N), and it's also in NP.
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So, many researchers don't use terms with due care. And many article are rejected by Nature.
I don't really get the love for Nature but here is an example that uses "runtime" in this sense in Nature Computational Science https://www.nature.com/articles/s43588-023-00589-x Here is one in Nature Physics https://www.nature.com/articles/s41567-023-02325-8 Here is one in Nature Precedings https://www.nature.com/articles/npre.2011.5593.1 And here is one in Nature Communications https://www.nature.com/articles/s4146…
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Why would it need to replace them? From the article, they claim they have found a way to reduce the upperbound faster when searching large Integer problems. I don't see how that effects the current searching process. All of these solvers you can enter in an upperbound yourself if you have knowledge of the problem and know a previous solution. So it seems if this is just a programmatic way of reducing the upper bound,…
Honestly? The search for the 'exactly optimal solution' is way overrated I think you can get a moderately efficient solution using heuristics at 1/10 of the time or less Not to mention developer time and trying to figure out which constraints make your problem infeasible. Especially as they get more complicated because you want to make everything linear
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A CS degree also qualifies you for on-the-job training in writing code, that odious task that your professors find trivial but somehow are also terrible at it.
We just don't have time. Incentives are elsewhere. Any time devoted to writing good code for a paper is time we cannot use to work on the next paper, (shudder) grant application, or a plethora of other things that we are either forced or incentivized to do. I miss coding from when I was in a more junior stage of my career and could afford time for it, and I think my fellow professors mostly feel the same, I don't thi…
But when those junior engineers hit my company, they can do homework problems and that’s about it. “CS fundamentals” aren’t useful when you can’t quit vi or debug a regex. They get to be useful 2-3 years later, after the engineer has shaken off being a student.
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> results of decades of incremental improvements. Gurobi was only founded in 2008. I don't doubt the optimizer was the result of "decades of incremental improvements", but the actual implementation must have been started relatively recently.
It was founded by some of the key people behind CPLEX (another solver, founded in 1987). In fact, one of the cofounders of Gurobi was a cofounder of CPLEX prior. They brought decades of knowledge with them.