Computational Science Stack Exchange is a question and answer site for scientists using computers to solve scientific problems. It's 100% free, no registration required.

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

I am looking for some clues for an optimization problem.

My problem consists on arriving to a image by optimizing multiple layers with the pixel position probability.

This is an overview of the problem: Please see the overview here

The first image on the left is a zoom of the greyscale image with only two grey values, followed by the probability of each cell taking one value of grey, and the same probability for the value black. Finally, I am looking for a way to arrive to the start image by using only the two layers.

In the end, I will be working with a complete image like this one:

Please see the complete image here

I understand that this will not give me a great result, and that I need to add constraints to my problem, however I am completely lost on the subject. I have found a lot of documentation for optimization, but not a good tutorial or examples for optimization problems defined in an Euclidean space. Is there documentation that I could read, a basic tutorial or code examples?

share|improve this question
I guess I'm confused as to what you want when you say you want to treat this as a multi-objective optimization problem. What would be your multiple objective functions? – Geoff Oxberry Jan 9 '12 at 21:19
I can't make heads or tails of this question in its current form. Please revise by adding some more examples and discussion, as this looks like an interesting problem. – Aron Ahmadia Jan 11 '12 at 10:28
Do you have a specific set of cost functions associated with your problem? Or are you trying to formulate your cost functions and constraints? – Paul Jan 15 '13 at 1:25
up vote 2 down vote accepted

I believe you should clarify your question a bit more. But the way most optimization problems work is that you need to define a function (cost function) that you need to optimize. Now this function might have multiple parameters or objectives that depend on the problem you are solving. Once you have identified the function, you should use an optimization algorithm to find desired values of the parameters based on the objectives. Some of the methods you can use are Evolutionary Algorithms such as Differential Evolution, Genetic Algorithms or Particle Swarm. You could also use other gradient based methods about which you can probably find extensive literature via Google. I used the following references during my research on shape optimization in aerodynamics:

Hope this helps.

share|improve this answer
Thanks for the help @bamdadhosseini! I am new to all these subjects, so the question ended up to be too vague. – A.R Jan 10 '12 at 9:07
yes, you might want to edit your question again and give us a better idea what you're having trouble with. – bamdadhosseini Jan 10 '12 at 21:55

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.