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.
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