Name
Abstract | ||
|---|---|---|
We extend the well-known BFGS quasi-Newton method and its limited-memory variant LBFGS to the optimization of non-smooth convex objectives. This is done in a rigorous fashion by generalizing three components of BFGS to subdifferentials: The local quadratic model, the identification of a descent direction, and the Wolfe line search conditions. We apply the resulting subLBFGS algorithm to L2-regularized risk minimization with binary hinge loss, and its direction-finding component to L1-regularized risk minimization with logistic loss. In both settings our generic algorithms perform comparable to or better than their counterparts in specialized state-of-the-art solvers. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1390156.1390309 | ICML |
Keywords | Field | DocType |
descent direction,local quadratic model,quasi-newton approach,convex optimization,l1-regularized risk minimization,direction-finding component,logistic loss,well-known bfgs quasi-newton method,l2-regularized risk minimization,limited-memory variant lbfgs,wolfe line search condition,generic algorithm,line search,quasi newton method | Mathematical optimization,Hinge loss,Computer science,Descent direction,Regular polygon,Line search,Minification,Broyden–Fletcher–Goldfarb–Shanno algorithm,Convex optimization,Wolfe conditions | Conference |
Citations | PageRank | References |
14 | 1.29 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Yu | 1 | 14 | 1.29 |
| S. V. N. Vishwanathan | 2 | 1991 | 131.90 |
| Simon Günter | 3 | 588 | 34.93 |
| Nicol N. Schraudolph | 4 | 1185 | 164.26 |