建立在割线条件上的新Liu-Storey型共轭梯度法的全局收敛性(英文)

我们基于拟牛顿法的割线条件提出两种LS型共轭梯度法.有趣的是,我们提出的方法中对于βk的计算公式与戴和廖[3]提出的有相似的结构.但是,新方法能够在合理的假设下保证充分下降性,这一点是戴-廖方法所不具备的.在强Wolfe线搜索下,给出了新方法的全局收敛结果.数值结果论证了该方法的有效性.

基于修正割线条件的NDL方法

基于带有自调比参数的修正DL方法,提出一个带有修正割线条件的修正共轭梯度法NDL方法.在满足Wolfe线搜索的条件下,证明了该方法具有充分下降性;最后分析并且证明,该方法在满足强Wolfe条件下,对一般函数都会具有全局收敛性.

基于修正割线条件的有限记忆BFGS方法

有限记忆BFGS(broyden-fletcher-goldfarb-shanno)方法主要用来求解大规模无约束优化问题,是最有效的拟牛顿方法之一,该方法利用存储一定量的向量对去克服拟牛顿方法需要存储大量矩阵的缺点,同时还保持了良好的收敛性质.通过修正割线条件改进有限记忆BFGS方法,使其具有更好的二阶曲率信息,并证明该方法对一致凸函数具有全局收敛性.与标准有限记忆BFGS方法(limited BFGS,LBFGS)相比,修正有限记忆BFGS方法(modified limited BFGS,MLBFGS)在计算上略有优势.

Statistical segmentation model on lattices

To reduce the difficulty of implementation and shorten the runtime of the traditional Kim-Fisher model, an entirely discrete Kim-Fisher-like model on lattices is proposed. The discrete model is directly built on the lattices, and the greedy algorithm is used in the implementation to continually decrease the energy function. First, regarding the gray values in images as discrete-valued random variables makes it possible to make a much simpler estimation of conditional entropy. Secondly, a uniform method within the level set framework for two-phase and multiphase segmentations without extension is presented. Finally, a more accurate approximation to the curve length on lattices with multi-labels is proposed. The experimental results show that, compared with the continuous Kim-Fisher model, the proposed model can obtain comparative results, while the implementation is much simpler and the runtime is dramatically reduced.

无约束优化一个修正的HS三项共轭梯度法

针对无约束优化问题,本文提出了一个修正的HS三项共轭梯度法。基于拟牛顿方程新型割线条件,提出了具有充分下降性质的改进HS三项共轭梯度参数公式。在弱Wolfe-Powell线搜索技术下,获得了算法的全局收敛性。数值实验结果表明该算法是有效的。