For the Mathematicians.

[doc hawk]

Roger rumbled,

> Thank you very much for your reply.

You’re quite welcome.

Accumulated knowledge is wasted if not shared!

I used to find it online quite easily.  But not any more.

>Again I thank you for taking the time to respond. Is your dissertation readable to a LiveCoder that has no experience in any other programming language?

The code  Fortran, so  it should be readable.

The descriptions are probably largely accessible, with 2d and I think 3d graphics to illustrate.

But the math for the underlying problem. . . I looked at it two or three years later, and . . . I was quite impressed with the math, could see *why* it was right, but generally had *no* idea why I ever would have thought to make those steps!

It would go on for two or three pages of matrix calculus at times.  And within those were multinomial factors 

You don’t need the underlying math of the genetic problem to make sense of the algorithm, though.  

I just found that it can now be downloaded.  Chapter 3 seems to be the guts of the algorithm.  It certainly came from googling the full title below.

Btw, my undergrad was in physics, then law school and practicing, before returning for the Ph.D. jointly in Econo9mics & Statistics, a few years at a university, and returning to law to pay tuition for my own kids . . .

I think I got to it for download from http://dissertation.com/abstracts/1701716 <http://dissertation.com/abstracts/1701716>.

And some info at:

https://www.econ.iastate.edu/RePEc/isu/genstf/genstf_4657.rdf <https://www.econ.iastate.edu/RePEc/isu/genstf/genstf_4657.rdf>


Template-Type: ReDIF-Paper 1.0
Title: Numerical optimization of recursive systems of equations with an application to optimal swine genetic selection
Author-Name: Hawkins, Richard Edmund
Abstract: A new dynamic programming method is developed for numerical optimization of recursive systems of equations, in which continuous choice variables determine the allowed choices in subsequent stages of the problem. The method works by dynamically creating bubbles, or subspaces, of the total search space, allowing the indexing of states visited for later use, and taking advantage of the fact that states adjacent to a visited state are likely to be visited. The method thereby allows search of spaces far larger than would traditionally be permitted by memory limitations. The search allows an infinite planning horizon, and tests at each stage to determine whether further optimization is worth the costs, reverting to a default choice when no longer profitable. The method is applied to the quantitative genetics problem of finding the optimal selection choices for quantitative traits using an identified locus, using the present discounted value of all generations. The method is then applied to the Estrogen Receptor Gene (ESR) to find the economic value of testing for this particular gene.
Creation-Date: 1999-01-01
File-URL: https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=13457&context=rtd
Number: 1999010108000013457
Handle: RePEc:isu:genstf:1999010108000013457