Name
Title | ||
|---|---|---|
Iterative projection algorithms for solving constraint satisfaction problems: Effect of constraint convexity |
Abstract | ||
|---|---|---|
Many inverse problems in imaging involve solving an optimization problem. In many cases, the problem is high-dimensional and non-convex, requiring the solution of a difficult, non-convex, global optimization problem. Such problems can be made tractable by enforcing hard constraints and treating the problem as a constraint satisfaction problem to locate a global solution, which can be refined using soft constraints if necessary. Iterative projection algorithms are an effective way of solving non-convex constraint satisfaction problems. The difficulty of solution, and the performance of these algorithms, depends on the degree of non-convexity of the constraints. Here we use simulations of a phase retrieval problem to study the performance of an iterative projection algorithm, the difference map algorithm, to study performance as a function of non-convexity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IVCNZ48456.2019.8960967 | 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) |
Keywords | Field | DocType |
Iterative projection algorithms,constraint satisfaction,phase retrieval,inverse problems,optimization | Constraint satisfaction,Convexity,Phase retrieval,Dykstra's projection algorithm,Computer science,Algorithm,Constraint satisfaction problem,Inverse problem,Optimization problem,Difference-map algorithm | Conference |
ISSN | ISBN | Citations |
2151-2191 | 978-1-7281-4188-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. P. Millane | 1 | 28 | 6.06 |
| Joshua T. Taylor | 2 | 0 | 0.34 |
| Romain D. Arnal | 3 | 0 | 0.68 |
| David H. Wojtas | 4 | 0 | 1.35 |
| Richard M. Clare | 5 | 0 | 0.34 |