How to accelerate progress in mathematical research? After completing a Ph.D. in pure mathematics, 10 years ago I left academia for working in industry. There, a typical question is "What can we do to accelerate $x$?" when a project is slowed down, and the typical answer is "Let the people concerned with the issue focus on it and/or bring in some experts", which usually solves the issue. 
I wonder if mathematical research can work the same way. Say, if you had $100 million to spare and really wanted to see the Riemann hypothesis resolved, what would you do? 


*

*Would it help to finance a special decade at some institution, where 25 leading researchers are free from everyday concerns (in particular, administrative and teaching duties) and can spend their entire time working on this problem together?

*Would it be better to use these funds to let the 25 experts each supervise 10 graduate students over a course of 20 years? Or to support some sort of crowdsourcing?

*Or is it just not possible to focus exclusively on one (incredibly difficult) problem and one should rather pursue whatever is doable at the moment? Is it similar to (paraphrasing Don Knuth) "Computer science is like the Great Wall of China where each workman contributes a brick"?
 A: Here's one budget for spending the money over 10 years. Obviously all the numbers are only indicative.
$\$$75 million for child care for mathematically trained people who want to work on these issues, an average of $\$$7,500 per year for 10 years for 1000 people each year. Household cleaning and food preparation could also be included. This would free up the time of current researchers, and open up the research to mathematically trained people, especially women, who are spending their time on work in the household instead of research.
$\$$10 million as prize money for unconditional proofs of known consequences of either the Riemann or Generalized Riemann Hypothesis. Perhaps this would be 20 prizes, each worth $\$$500,000, based on the lists of consequences here, here or here. The ideas in those proofs would be good sources ideas for proving the Riemann hypothesis.
$\$$10 million in travel grants to encourage global collaboration on these topics. Perhaps this would be 10 years of 400 grants per year of $\$$2,500 each, covering airfare and a week of expenses in each case.
$\$$4 million to help people write up their research or their expository works in the area. Perhaps this means that for each of 10 years there are 10 people being paid an average of $\$$40,000 per year to help write up this work.
$\$$1 million to make existing numerical research in the area more accessible, e.g. better access to tables of the zeroes, translations of relevant algorithms into nice packages in several languages.
Note all of this money may go further in countries with high levels of mathematics but cheaper costs of living. Conditions for work on the Riemann hypothesis are already relatively good for math professors at American or European universities; to make a big step forward, it may help to involve people who are mathematically talented but not in those roles, for whatever reason.
A: Wikipedia is a great project, and it is without doubt a big impactful resource. With this as inspiration, 
I started to collect definitions, theorems, formulas and references together with some examples, for topics regarding symmetric functions. This is skewed towards more personal interests and a bit too technical to be on wikipedia.
This has accelerated my personal research projects, as I can refer new collaborators to this page, instead of asking them to find the correct page in a book or paywalled article.
I also try to keep up with the latest research, so that the information is fresh, and quickly available.
Having quick access to definitions and references, which are easily found by search engines, and viewable on a regular web page with a smartphone, should help facilitate quicker progress.
The conclusion is, funding an online resource with the purpose to quickly get a new PhD student or researcher new to the field up to speed, is probably a good investment.
Aggregating and streamlining learning existing results and knowledge is a good step to produce new knowledge, in my opinion.
