Name
Abstract | ||
|---|---|---|
Two new optimization techniques based on projections onto convex space (POCS) framework for solving convex optimization problems are presented. The dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. If the cost function is a convex function in R^N the corresponding set is also a convex set in R^{N+1}. The iterative optimization approach starts with an arbitrary initial estimate in R^{N+1} and an orthogonal projection is performed onto one of the sets in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation (TV), filtered variation (FV), L_1, and entropic cost functions. A new denoising algorithm using the TV framework is developed. The new algorithm does not require any of the regularization parameter adjustment. Simulation examples are presented. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Discrete mathematics,Combinatorics,Dykstra's projection algorithm,Convex set,Subderivative,Convex function,Conic optimization,Proper convex function,Convex optimization,Mathematics,Convex analysis |
DocType | Volume | Citations |
Journal | abs/1309.0700 | 1 |
PageRank | References | Authors |
0.37 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Tofighi | 1 | 65 | 8.74 |
| Kivanç Köse | 2 | 75 | 9.55 |
| A. Enis Çetin | 3 | 871 | 118.56 |