I'm looking for a solid reference (or references) on numerical optimization techniques aimed at statisticians, that is, it would apply these methods to some standard inferential problems (eg MAP/MLE in common models). Things like gradient descent (straight and stochastic), EM and its spinoffs/generalizations, simulated annealing, etc.
I'm hoping it would have some practical notes on implementation (so often lacking in papers). It doesn't have to be completely explicit but should at least provide a solid bibliography.
Some cursory searching turned up a couple of texts: Numerical Analysis for Statisticians by Ken Lange and Numerical Methods of Statistics by John Monahan. Reviews of each seem mixed (and sparse). Of the two a perusal of the table of contents suggests the 2nd edition of Lange's book is closest to what I'm after.