(sorry John, I sent my last email before getting the digest)
Perfect.  Since a snapshot of the interfaces  exists for 1.35, then a port over to newer versions of boost is reasonable.  Hopefully we can turn this into a top level project that does the baseline optimization/root finding people need before shopping for really specific tools/algorithms.
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A couple of other questions on this beautiful distributions library - which I see as a great starting point for a lot of operations treating them as measures:
* Is there any planned or existing support in the library for multi-dimensional distributions?  In particular, bivariate norms with a covariance matrix passed in?  Will the library design and notation support these sorts of extensions without too many problems?
* Is there any way in the current library to pass in a user created discrete valued probability measure and have it as a "distribution" here?  If not, I think I may write it, but as I am not a very good library programmer I would love to exploit any other work out there.  I would need this to work for at least 1 and 2 dimension measures.
  * If we were to use a function to bundle a discrete valued function, is there a nice class/pattern out there that already combines the x-values and y-values into a single class? (I also want this later for implementing different interpolation algorithms returning functors after passing in a discrete function in the constructor).
* Then I would write a specialization of this (2nd and higher dimension) for a markov measure.
* Then, I want to write a general operator for unconditional expectation passing in a function object, and over a generic distribution as a measure from this library (though I will use it for the discrete measures to begin with).  Specializing for different types of distributions, this could use exact analytical calculations, quadrature, or use monte-carlo methods.
* Last, I want to write a conditional expectation operator that takes in a function object mapping reals^2 to reals, a distribution (would have to be 2 dimensional), the conditional (which would be a real for now, but could be generic), and what dimension the condition applies to.
* Eventually, we may be able to tie together this operator with boost::lambda and boost::bind and we could up with a very elegant way to write out functions that involve conditional expectations.  It may not have the performance of hand-tweaked fortran loops, but it should be a good enough place to start and infinitely preferable to matlab.

The main use case I have for this is to implement easy/flexible notation for conditional distribution functions when writing out bellman equations for dynamic programming in economics.  Most cases of this involve two dimensional markov measures (often approximated by Tauchen's algorithm from ARMA processes)

If anyone is interested in this topic, has ideas, wants to work jointly, or plans to tell me that I am insane, please email me.  My main fear is that I am a library user with a pretty good ability to think in generics, but with absolutely no chance of ever understanding what all of the crazy non-algorithmic code in these libraries does or writing it myself.

-Jesse