在图像处理,机器学习和工程学等应用领域中,通常需要处理一些定义在Hilbert空间中的大规模算子方程。为求解这类算子方程及最值问题,构造了一类增量型不精确Broyden方法并证明了该算法的线性收敛和局部超线性收敛性。该算法降低了在处...在图像处理,机器学习和工程学等应用领域中,通常需要处理一些定义在Hilbert空间中的大规模算子方程。为求解这类算子方程及最值问题,构造了一类增量型不精确Broyden方法并证明了该算法的线性收敛和局部超线性收敛性。该算法降低了在处理大规模问题中所产生的储存成本,并通过应用证明了该算法的有效性。In application fields such as image processing, machine learning, and engineering, it is often necessary to solve large-scale operator equations defined in Hilbert spaces. To address such operator equations and optimization problems, a class of incremental inexact Broyden methods has been developed, and the linear convergence as well as local superlinear convergence of this algorithm has been proven. This algorithm reduces the storage costs associated with handling large-scale problems, and its effectiveness has been demonstrated through practical applications.展开更多
文摘在图像处理,机器学习和工程学等应用领域中,通常需要处理一些定义在Hilbert空间中的大规模算子方程。为求解这类算子方程及最值问题,构造了一类增量型不精确Broyden方法并证明了该算法的线性收敛和局部超线性收敛性。该算法降低了在处理大规模问题中所产生的储存成本,并通过应用证明了该算法的有效性。In application fields such as image processing, machine learning, and engineering, it is often necessary to solve large-scale operator equations defined in Hilbert spaces. To address such operator equations and optimization problems, a class of incremental inexact Broyden methods has been developed, and the linear convergence as well as local superlinear convergence of this algorithm has been proven. This algorithm reduces the storage costs associated with handling large-scale problems, and its effectiveness has been demonstrated through practical applications.
