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Xref: csiph.com comp.lang.python:22409

On 03/21/2012 03:55 AM, Nathan Rice wrote:
> In mathematics, when you perform global optimization you must be
> willing to make moves in the solution space that may result in a
> temporary reduction of your optimality condition.  If you just perform
> naive gradient decent, only looking to the change that will induce the
> greatest immediate improvement in optimality, you will usually end up
> orbiting around a solution which is not globally optimal.  I mention
> this because any readability or usability information gained using
> trained programmers is simultaneously measuring the readability or
> usability and its conformance to the programmer's cognitive model of
> programming.  The result is local optimization around the
> current-paradigm minimum.  This is why we have so many nearly
> identical curly brace C-like languages.

I think you've just described that greedy algorithm can't always find 
the globally optimal solution.