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Description: L-M优化算法（trainlm）和贝叶斯正则化算法（trainbr）-LM optimization algorithm (trainlm) and Bayesian regularization algorithm (trainbr)
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Size: 1024 |
Author: ouyinghua |
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Description: BFGS算法: 可以解决无约束的最优化问题，如求目标函数的极值等。-BFGS algorithm: can solve unconstrained optimization problems, such as seeking the extreme, such as the objective function.
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Size: 13312 |
Author: 贺鹏 |
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Description: This library is a C port of the implementation of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method written by Jorge Nocedal. The original FORTRAN source code is available at:
This library is a C port of the 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
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Size: 333824 |
Author: Yu-Chieh Wu |
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Description: Software for Large-scale Bound-constrained or Unconstrained Optimization
L-BFGS-B is a limited-memory quasi-Newton code for large-scale bound-constrained or unconstrained optimization. The code has been developed at the Optimization Technology Center, a joint venture of Argonne National Laboratory and Northwestern University.
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Size: 266240 |
Author: 徐志平 |
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Description: 自己编的，实现l-bfgs解无约束优化问题-Own, and the realization of l-bfgs Unconstrained optimization problems
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Size: 5120 |
Author: 陈博 |
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Description: L-BFGS用于内存紧张的系统中，可以用于求解大规模数据集的优化-Software for Large-scale Unconstrained Optimization L-BFGS is a limited-memory quasi-Newton code for large-scale unconstrained optimization.
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Size: 25600 |
Author: 奕风 |
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Description: 这是一个快速的拟牛顿法程序，非常实用，非常强大-FMINLBFGS is a Memory efficient optimizer for problems such as image registration with large amounts of unknowns, and cpu-expensive gradients.
Supported:
- Quasi Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS).
- Limited memory BFGS (L-BFGS).
- Steepest Gradient Descent optimization.
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Size: 10240 |
Author: 奕风 |
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Description: This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes:
Conditional Maximum Entropy Model
L-BFGS Parameter Estimation
GIS Parameter Estimation
Gaussian Prior Smoothing
C++ API
Python Extension module
Document and Tutorial -)-This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes:
Conditional Maximum Entropy Model
L-BFGS Parameter Estimation
GIS Parameter Estimation
Gaussian Prior Smoothing
C++ API
Python Extension module
Document and Tutorial -)
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Size: 764928 |
Author: shabo |
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Description: L-BFGS的c库文件，可以求最优化规划问题，非常好用，欢迎下载。-libLBFGS: a library of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)
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Size: 318464 |
Author: wu haifeng |
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Description: L-BFGS的相关资料，很齐全，共5个文件
-Of L-BFGS-related information, it is complete
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Size: 979968 |
Author: 李森 |
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Description: 可以实现大规模的bfgs功能，进行目标函数的最优化求解，即L-BFGS-Can achieve large scale bfgs function, the objective function is the most optimal solution, ie, L-BFGS
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Size: 18432 |
Author: wf |
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Description: 一个有限内存Broyden-Fletcher-Goldfarb-Shanno函数半二次优化工具，可以用来进行求解函数最优值。-libLBFGS is a C port of the implementation of Limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method, which can be used to solve the optimization
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Size: 273408 |
Author: hzw |
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Description: Hidden-Unit Conditional Random Fields
工具箱，可以用于训练linearCRF和和L.J.P. van der Maaten, M. Welling
提出的huCRF-We provide Matlab code that implements the training and evaluation of hidden-unit CRFs, as well as code to reproduce the results of our experiments. The code implements four different training algorithms: (1) a batch learner that uses L-BFGS, (2) a stochastic gradient descent learner, (3) an online perceptron training algorithm, and (4) an online large-margin perceptron algorithm. The code can also be used to perform (conditional) herding in hidden-unit CRFs.
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Size: 165888 |
Author: 王磊 |
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Description: CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4 - 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.,CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4- 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.
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Size: 29696 |
Author: icypriest |
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Description: CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4 - 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.,CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4- 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.
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Size: 1804288 |
Author: icypriest |
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Description: 无约束优化中非常有用的L-BFGS代码,在解决大规模优化问题中,有着良好的数值表现.-useful unconstrained optimization L-BFGS code, in the solution of large-scale optimization problems, has a good numerical performance.
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Size: 837632 |
Author: 阿新 |
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Description: 本程序是数值计算中FGS方法的改进型BFGS的代码，可以直接使用-This program is a modified BFGS method of numerical calculation FGS code can be used directly
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Size: 10240 |
Author: 王龙 |
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Description: 拟牛顿算法计算函数最小值，采用LBFGS算法，亲测可用。-a C port of the implementation of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method written by Jorge Nocedal.
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Size: 318464 |
Author: lioto |
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Description: L-BFGS有限内存BFGS法,用于求解大规模无约束最优化问题-L-BFGS method for large scale unconstrained optimization
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Size: 19456 |
Author: 周群艳 |
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Description: CSAMT一维反演程序，基于L-BFGS方法的水平电偶极一维反演-CSAMT one dimensional inversion procedure, based on L- BFGS method of horizontal electric dipole one dimensional inversion
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Size: 912384 |
Author: lisirui |
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