https://hackerpaper.kaipfister.de/p/2206.09787/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=41281640urn:hackerpapers:mention:2206.09787:41281640Sun, 18 Aug 2024 11:15:57 GMT<p>The paper I have mentioned can be found here https://arxiv.org/pdf/2206.09787 There are so many things that have only been invented in the last couple of years like RINS, MCF cuts, conflict analysis, symmetry detection, dynamic search,... (see e.g. Tobias Achterberg&#x27;s line of work). On the other hand, hardware improvements were not as relevant for LP and MILP solvers as one would expect: For instance, as of now there is still no solver that really uses GPU compute (though people are working on that). The reason is that parallelization of simplex solvers is quite though since the algorithm is…</p><p><a href="https://hackerpaper.kaipfister.de/p/2206.09787/">Paper and discussion history</a>. A mention is not an endorsement.</p>math.OChttps://news.ycombinator.com/item?id=32287045urn:hackerpapers:mention:2206.09787:32287045Sat, 30 Jul 2022 14:25:33 GMT<p>&gt; we examine the contribution of more computing power to better outcomes No, they pick a set of problems where computational methods are known to have a beneficial impact and the plot every progress in that field against increased amounts of computing. Since amount of computing power used is monotonous and ELO score/Go performance/weather prediction success is trending monotonous the correlation is pretty high. However computation power is not the only thing that rose mostly monotonically during that time. At best they derived an upper bound of the contribution of more computing power to…</p><p><a href="https://hackerpaper.kaipfister.de/p/2206.09787/">Paper and discussion history</a>. A mention is not an endorsement.</p>math.OC