On the rate of convergence of the proximal alternating linearized minimization algorithm for convex problems.
Ron ShefiMarc TeboullePublished in: EURO J. Comput. Optim. (2016)
Keyphrases
- iterative algorithms
- learning algorithm
- dynamic programming
- benchmark problems
- objective function
- quadratic optimization problems
- convergence rate
- combinatorial optimization
- detection algorithm
- convex hull
- gauss seidel method
- minimization problems
- stationary points
- globally optimal
- optimization problems
- k means
- computational complexity
- simulated annealing
- worst case
- probabilistic model
- piecewise linear
- cost function
- linear programming
- number of iterations required
- constrained minimization
- linear equations
- optimal solution
- stochastic shortest path
- search space
- evolutionary algorithm
- convergence analysis
- optimization method
- kalman filter
- image restoration