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1.
In this paper, we study the local linear convergence properties of a versatile class of Primal–Dual splitting methods for minimizing composite non-smooth convex optimization problems. Under the assumption that the non-smooth components of the problem are partly smooth relative to smooth manifolds, we present a unified local convergence analysis framework for these methods. More precisely, in our framework, we first show that (i) the sequences generated by Primal–Dual splitting methods identify a pair of primal and dual smooth manifolds in a finite number of iterations, and then (ii) enter a local linear convergence regime, which is characterized based on the structure of the underlying active smooth manifolds. We also show how our results for Primal–Dual splitting can be specialized to cover existing ones on Forward–Backward splitting and Douglas–Rachford splitting/ADMM (alternating direction methods of multipliers). Moreover, based on these obtained local convergence analysis result, several practical acceleration techniques are discussed. To exemplify the usefulness of the obtained result, we consider several concrete numerical experiments arising from fields including signal/image processing, inverse problems and machine learning. The demonstration not only verifies the local linear convergence behaviour of Primal–Dual splitting methods, but also the insights on how to accelerate them in practice. 相似文献
2.
An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines 总被引:2,自引:0,他引:2
Luca Zanni 《Computational Management Science》2006,3(2):131-145
In this paper we propose some improvements to a recent decomposition technique for the large quadratic program arising in
training support vector machines. As standard decomposition approaches, the technique we consider is based on the idea to
optimize, at each iteration, a subset of the variables through the solution of a quadratic programming subproblem. The innovative
features of this approach consist in using a very effective gradient projection method for the inner subproblems and a special
rule for selecting the variables to be optimized at each step. These features allow to obtain promising performance by decomposing
the problem into few large subproblems instead of many small subproblems as usually done by other decomposition schemes. We
improve this technique by introducing a new inner solver and a simple strategy for reducing the computational cost of each
iteration. We evaluate the effectiveness of these improvements by solving large-scale benchmark problems and by comparison
with a widely used decomposition package. 相似文献
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4.
考虑约束最优化问题:minx∈Ωf(x)其中:f:R^n→R是连续可微函数,Ω是一闭凸集。本文研究了解决此问题的梯度投影方法,在步长的选取时采用了一种新的策略,在较弱的条件下,证明了梯度投影响方法的全局收敛性。 相似文献
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7.
Robust linear optimization under general norms 总被引:1,自引:0,他引:1
We explicitly characterize the robust counterpart of a linear programming problem with uncertainty set described by an arbitrary norm. Our approach encompasses several approaches from the literature and provides guarantees for constraint violation under probabilistic models that allow arbitrary dependencies in the distribution of the uncertain coefficients. 相似文献
8.
Xu Tianzhou 《东北数学》1997,(1)
ModulesoverOperatorAlgebrasandItsModuleMapsXuTianzhou(许天周)(DepartmentofMathematics,NanjingUniersity,Nanjing,210093)AbstractLe... 相似文献
9.
采用MSDI严格角动量投影46Ti、48Cr形变HF谱 总被引:1,自引:1,他引:0
采用修正的表面δ相互作用(MSDI),以球形壳模型单粒子态作基矢,对fp壳层区偶偶核46Ti、48Cr进行形变Hartree–Fock(HF)计算,并用形变HF单粒子态构造Slater行列式波函数,即形变HF内禀态,然后对其实施严格角动量投影程序,得到比较合理的结果. 相似文献
10.
本文给出了Banach空间广义分解定理的一个初等证明,并利用它来证明两个对称不等式.这是首次在Banach空间获得这样的不等式. 相似文献