Name
Abstract | ||
|---|---|---|
Convex programming involves a convex set F Rn and a convex cost function c : F ! R. The goal of convex programming is to nd a point in F which minimizes c. In online convex programming, the convex set is known in advance, but in each step of some repeated optimization problem, one must select a point inF before seeing the cost function for that step. This can be used to model factory production, farm production, and many other industrial optimization prob- lems where one is unaware of the value of the items produced until they have already been constructed. We introduce an algorithm for this domain. We also apply this algorithm to repeated games, and show that it is re- ally a generalization of innitesimal gradient ascent, and the results here imply that gen- eralized innitesimal gradient ascent (GIGA) is universally consistent. |
Year | Venue | Keywords |
|---|---|---|
2003 | International Conference on Machine Learning | optimization problem,convex programming,online algorithms,convex set,cost function,repeated game |
Field | DocType | Citations |
Discrete mathematics,Mathematical optimization,Convex combination,Convex hull,Convex set,Subderivative,Proper convex function,Conic optimization,Convex optimization,Convex analysis,Mathematics | Conference | 589 |
PageRank | References | Authors |
72.98 | 14 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Zinkevich | 1 | 1893 | 160.99 |