Reputation: 159
I'm looking for a Java library to solve this problem:
We know X is sparse(most of it's entries are zero), so X can be recovered by solving this:
variable X;
minimize(norm(X,1)+norm(A*X - Y,2));
It's a MATLAB code, matrix A and vector Y are known and I want the best X.
I saw JOptimizer, but I couldn't use it. (Doesn't have good documentation or examples).
Upvotes: 2
Views: 2550
Reputation: 11222
To solve convex optimization problems in java you can use the following library https://github.com/erikerlandson/gibbous
Upvotes: 0
Reputation: 2641
As far as I can tell, you're trying to solve a binary integer program for feasibility
Ax = b, x in {0,1}.
I'm not completely sure, but it seems that you might be interested in the optimization problem
min 1'*x
s.t. Ax = b, x in {0,1}
where 1 is a vector of 1's of the same dimension as x.
The feasibility problem may be in practice much easier than the optimization problem - it all depends on a particular A and b.
If you can get a license of either CPLEX or Gurobi (if you're an academic), these are excellent integer programming solvers with good Java API's. If you don't have access to these, lpsolve may be a good option.
As far as I can tell, JOptimizer will not solve your problem since your variables are integers (although I have never used JOptimizer).
Upvotes: 1
Reputation: 22516
What you need is a reasonably good LP Solver.
If you have access to CPLEX (not-free), its Java API would work great.
Also, you can look into SuanShu, a Java numerical and statistical library
lpSolve has a Java wrapper which can do the job.
Finally, JOptimizer is indeed a good option. Not sure if you looked at this example.
Hope at least one of those help.
Upvotes: 2