Name
Abstract | ||
|---|---|---|
In this paper, we introduce the regularized Newton method for multiobjective optimization. The method does not scalarize the original multiobjective optimization problem. For any vector convex function, with a compact level set, the regularized Newton method generates a sequence that converges to the optimal points from any starting point. Moreover the regularized Newton method does not require strong convexity property in the entire space. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CSO.2012.94 | CSO |
Keywords | Field | DocType |
optimal point,compact level set,regularized newton method,multiobjective optimization,entire space,strong convexity property,vector convex function,original multiobjective optimization problem,level set,convex functions,convex programming,convergence,optimization,newton method,vectors | Mathematical optimization,Subderivative,Newton's method in optimization,Conic optimization,Proper convex function,Convex optimization,Mathematics,Convex analysis,Steffensen's method,Newton's method | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhijie Wang | 1 | 2 | 4.76 |
| Sanming Liu | 2 | 3 | 2.56 |