Name
Abstract | ||
|---|---|---|
In this paper, we complement the framework of bilevel unconstrained minimizaconvergent as O(k-(3p+1)/2), where k is the iteration counter. As compared with [Y. Nesterov, Math. Program., to appear], we replace the auxiliary line search by a segment search. This allows us to bound its complexity by a logarithm of the desired accuracy. Each step in this search needs an approximate computation of a high-order proximal-point operator. Under the assumption on boundedness of (p+ 1)th derivative of the objective function, this can be done by one step of the pthorder augmented tensor method. In this way, for p = 2, we get a new second-order method with the rate of convergence O(k-7/2) and logarithmic complexity of the auxiliary search at each iteration. Another possibility is to compute the proximal-point operator by lower-order minimization methods. As an example, for p = 3, we consider the upper-level process convergent as O(k-5). Assuming the boundedness of fourth derivative, an appropriate approximation of the proximal-point operator can be computed by a second-order method in logarithmic number of iterations. This combination gives a second-order scheme with much better complexity than the existing theoretical limits. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1137/20M134705X | SIAM JOURNAL ON OPTIMIZATION |
Keywords | DocType | Volume |
convex optimization, tensor methods, proximal-point operator, lower complexity bounds, optimal methods | Journal | 31 |
Issue | ISSN | Citations |
4 | 1052-6234 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yurii Nesterov | 1 | 1800 | 168.77 |