I had to cancel a trip to Rutgers today for health reasons. My only research was some conversation with Magland about the relationship between the standard data-analysis practice of executing sets of sequential operations on data, and the concept of optimizing a scalar objective function. The context is spike sorting for neuroscience and catalog generation for astronomical imaging surveys.
When the data are complex and there is no simple, compact parametric model, it is hard to just optimize a likelihood or penalized likelihood or utility (though that doesn't stop us with The Tractor). However, sequential heuristic procedures can be designed to be some kind of locally greedy optimization of a scalar. That is, even if the code isn't explicitly an optimization, it can implicitly use the concept of optimization to set the values of otherwise arbitrary parameters (like detection thresholds, window sizes, and decision boundaries).
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