-1

Consider an optimization problem of some dimension n, Given some linear set of equations(inequalities) or constraints on the inputs which form a convex region, finding the maximum\minimum value of some expression which is some linear combination of inputs(or dimensions).

For larger dimension, these optimization problems take much time to give the exact answer.

So, can we use machine learning techniques, to get some approximate solution in lesser time.

if we can use machine learning techniques in this context, How the Training set should be??

Sahukari Ganesh
  • 51
  • 1
  • 7

2 Answers2

2

Do you mean "How big should the training set be?" If so, then that is very much a "how long is a piece of string" question. It needs to be large enough for the algorithm being used, and to represent the data that is being modeled.

winwaed
  • 7,645
  • 6
  • 36
  • 81
  • i mean what should be the format of training data, like all the data examples have to be taken from same convex region or similar – Sahukari Ganesh Jun 30 '11 at 15:37
1

This doesn't strike me as being especially focused on machine learning, as is typically meant by the term anyway. It's just a straightforward constrained optimization problem. You say that it takes too long to find solutions now, but you don't mention how you're trying to solve the problem.

The simplex algorithm is designed for this sort of problem, but it's exponential in the worst case. Is that what you're trying that's taking too long? If so, there are tons of metaheuristics that might perform well. Tabu search, simulated annealing, evolutionary algorithms, variable depth search, even simple multistart hill climbers. I would probably try something along those lines before I tried anything exotic.

deong
  • 3,820
  • 21
  • 18