Online Convex Programming and Generalized Infinitesimal Gradient Ascent

@inproceedings{Zinkevich2003OnlineCP,
  title={Online Convex Programming and Generalized Infinitesimal Gradient Ascent},
  author={Martin Zinkevich},
  booktitle={ICML},
  year={2003}
}
Convex programming involves a convex set F ⊆ R and a convex function c : F → R. The goal of convex programming is to find a point in F which minimizes c. In this paper, we introduce online convex programming. 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 in F before seeing the cost function for that step. This can be used to model factory production, farm production, and many other industrial… CONTINUE READING
Highly Influential
This paper has highly influenced 196 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 1,302 citations. REVIEW CITATIONS

Citations

Publications citing this paper.

1,302 Citations

0100200'05'08'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 1,302 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 28 references

Convex optimization. In press, available at http://www.stanford.edu/~boyd/cvxbook.html

  • S. Boyd
  • 2003

Online convex programming and generalized infinitesimal gradient ascent (Technical Report CMU-CS-03-110)

  • M. Zinkevich
  • 2003
2 Excerpts

Quadratic programming: Algorithms, anomolies, applications

  • J. Boot
  • 2003

Similar Papers

Loading similar papers…