Ahhh. Back on comfey ground for me...

Simulated Annealing and Adaptive Simulated Annealing are mathematical models commonly used in tough statistical problems such as finance, market prediction, physics, neural networking models, etc.

We use an ASA derivitive in analyzing temporal video quality over a particular compression algo with different scene constraints. Basically a Monte Carlo integration methodology...

The upshot is you are statistically guaranteed a nearly 'perfect' solution. The downfalls are: It's slow. It's computationally expensive. It's oft overused when there are better methods 'cause it's "cool". It's slow. It's rather tough to tune to a specific problem and other curve-fit techniques are often easier. And it's slower than the second coming.

Did I mention it's generally really slow?


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I appreciate your curiosity, and I don't want to be mean. But it seems like you're asking to pursue a grad course in math, biology, chemistry, physics, bioinformatics, and probably a few I've missed. That's cool and all, but you're lacking a few of the prerequisites to 'get it'

If you're THAT interested, you're going to need to do some homework. Hundreds of thousands of pages (millions?) have been written beating much of this to death. Cliffnotes are fine, but atleast in my area of specialty (math, numerical recipies and algorithmic design), they simply aren't going to get you anywhere that is of any use in understanding the very next question that was based on the one you asked just before.

I hope that doesn't come out tooo harsh.

My interest personally has been peaked in no small part by your questions. I find myself brushing up on my organic chem, biochem, molecular dynamics and pursing the relatively new (to me) field of bioinformatics. I thank you for that.

I'm more than willing to share my reading list with you - but you're likely to get frustrated if you aren't comfortable in atleast second year chem and second semester biology...