For many combinatorial problems however, improvements in algorithms can often produce bigger strides than just throwing brute force compute at the problem. Take Mixed Integer Programs (MIPs) -- roughly the optimization-equivalent of SATs -- used for airline scheduling, optimal assignment problems and such. In slide 12 [1] (there are other sources that corroborate), the author notes that MIP solver performance between 1988-2017 had improved 2,527,768,000x.

17,120x was due to machine improvements (single core). 147,650x was due to algorithmic improvements. Multiple cores can also provide a performance boost up to a point, before saturating due to coordination costs. The author notes that "A typical MIP that would have taken 124 years to solve in 1988 will solve in 1 second now".

The biggest improvements in MIP algorithm performance have been due to improvements in solver heuristics (!), because the fastest computations are those that don't have to be performed at all -- i.e. that are eliminated via heuristics.

[1] http://www.focapo-cpc.org/pdf/Linderoth.pdf

That is just... insane. I knew only vaguely that performance had improved significantly for many NP-hard/complete problems in practice, but I did not realize the magnitude of improvement, especially due to better algorithms.

> The biggest improvements in MIP algorithm performance have been due to improvements in solver heuristics (!), because the fastest computations are those that don't have to be performed at all -- i.e. that are eliminated via heuristics.

That is also... remarkable. Thank you for sharing.

I can't help but agree with you :-)

EDIT: Given that most of the "algorithmic" improvements have been due to better solver heuristics, I imagine it should be possible to train meta DL/RL models that learn how to find good heuristics for training DL models with high sample efficiency. Come to think of it, this competition seems to be asking precisely for such "black-box heuristic-guessing" models, so clearly there are people working on it.

But those improvements were also a product of lots of smart people funded by cash-rich industries (i.e. oil & gas, airlines... MIPs are big bucks commercially. I recall at one point a commercial CPLEX license was $100k list) poking at the problem for over 30 years. Many Ph.D.s in operations research and mathematical optimization were generated on this topic alone.

Note that now we have lots of smart people funded by new cash-rich industries (search, social networks, SaaS, etc.) poking at the problem with "black-box" approaches. I will be interesting to see what comes out of it.

In some distributed computational settings, memory traffic is actually the main bottleneck and redundant computations are executed to reduce the need to send data (a similar situation to the one you aptly describe).

I think that, in the case of hyperparameter/meta-learning optimization (or search, depending on how you think about it) we are at a time right now where the complexity of models which can effectively be put into production is a function of our ability to, at least partially, analyze the space of possible modeling decisions. Will we escape that, and have models whose training cost is less significant than the cost of executing an "intelligent" hyperparameter search process? Maybe ... I am a GP person so I see potential in clever analysis of circumstances so that RKHS methods (for instance) can be leveraged and simplify the training process. But the current trajectory of the community has been to work on increasingly expensive models, which makes the ability to effectively use them with limited tuning/search cost still relevant.

Otherwise I agree, Gaussian Processes are nice and friendly, and work quite well for low-dimensional search (e.g., from a few to hundreds of hyperparameters) under very natural, general assumptions :-)