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In this paper, we propose a new incremental gradient method for solving a regularized minimization problem whose objective is the sum of $m$ smooth functions and a (possibly nonsmooth) convex function. This method uses an adaptive stepsize. Recently proposed incremental gradient methods for a regularized minimization problem need $O(mn)$ storage, where $n$ is the number of variables. This is the drawback of them. But, the proposed new incremental gradient method requires only $O(n)$ storage.
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참고문헌 (31)
참고문헌 신청
D. P. Bertsekas / 1997 / A new class of incremental gradient methods for least squares problems / SIAM J. Optim 7(4):913~926
D. P. Bertsekas / 1999 / Nonlinear Programming, 2 / Athena Scientific
D. P. Bertsekas / 1989 / Parallel and Distributed Computation: Numerical Methods / Prentice-Hall
D. Blatt / 2007 / A convergent incremental gradient method with a constant step size / SIAM J. Optim 18(1):29~51
P. S. Bradley / 1999 / Mathematical programming for data mining: formulations and challenges / INFORMS J. Comput 11(3):217~238
D. P. Bertsekas / 1999 / Nonlinear Programming, 2 / Athena Scientific
D. P. Bertsekas / 1989 / Parallel and Distributed Computation: Numerical Methods / Prentice-Hall
D. Blatt / 2007 / A convergent incremental gradient method with a constant step size / SIAM J. Optim 18(1):29~51
P. S. Bradley / 1999 / Mathematical programming for data mining: formulations and challenges / INFORMS J. Comput 11(3):217~238
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