资源简介
实现Logistic Regression的开源代码,LBFGS应该是目前普遍被采用的一种方法,代码简洁,注释详细,不得不拥有!
代码片段和文件信息
/*
* Limited memory BFGS (L-BFGS).
*
* Copyright (c) 1990 Jorge Nocedal
* Copyright (c) 2007-2010 Naoaki Okazaki
* All rights reserved.
*
* Permission is hereby granted free of charge to any person obtaining a copy
* of this software and associated documentation files (the “Software“) to deal
* in the Software without restriction including without limitation the rights
* to use copy modify merge publish distribute sublicense and/or sell
* copies of the Software and to permit persons to whom the Software is
* furnished to do so subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED “AS IS“ WITHOUT WARRANTY OF ANY KIND EXPRESS OR
* IMPLIED INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM DAMAGES OR OTHER
* LIABILITY WHETHER IN AN ACTION OF CONTRACT TORT OR OTHERWISE ARISING FROM
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/* $Id$ */
/*
This library is a C port of the FORTRAN implementation of Limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method written by Jorge Nocedal.
The original FORTRAN source code is available at:
http://www.ece.northwestern.edu/~nocedal/lbfgs.html
The L-BFGS algorithm is described in:
- Jorge Nocedal.
Updating Quasi-Newton Matrices with Limited Storage.
Mathematics of Computation Vol. 35 No. 151 pp. 773--782 1980.
- Dong C. Liu and Jorge Nocedal.
On the limited memory BFGS method for large scale optimization.
Mathematical Programming B Vol. 45 No. 3 pp. 503-528 1989.
The line search algorithms used in this implementation are described in:
- John E. Dennis and Robert B. Schnabel.
Numerical Methods for Unconstrained Optimization and Nonlinear
Equations Englewood Cliffs 1983.
- Jorge J. More and David J. Thuente.
Line search algorithm with guaranteed sufficient decrease.
ACM Transactions on Mathematical Software (TOMS) Vol. 20 No. 3
pp. 286-307 1994.
This library also implements Orthant-Wise Limited-memory Quasi-Newton (OWL-QN)
method presented in:
- Galen Andrew and Jianfeng Gao.
Scalable training of L1-regularized log-linear models.
In Proceedings of the 24th International Conference on Machine
Learning (ICML 2007) pp. 33-40 2007.
I would like to thank the original author Jorge Nocedal who has been
distributing the effieicnt and explanatory implementation in an open source
licence.
*/
#ifdef HAVE_CONFIG_H
#include
#endif/*HAVE_CONFIG_H*/
#include
#include
#include
#include
#include
#ifdef _MSC_VER
#define inline _
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