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本文对一类带等式的非光滑最优化问题给出了一种逐次二次规划方法。这类问题的目标函数是非光滑合成函数,约束函数是非线性光滑函数。该方法通过逐次解二阶规划寻找搜索方向,使用l1-罚函数的非精确线搜索得到新的迭代点。我们证明了算法的全局收敛性并给出了数值试验结果。  相似文献
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本文给出了一类线性约束下不可微量优化问题的可行下降方法,这类问题的目标函数是凸函数和可微函数的合成函数,算法通过解系列二次规划寻找可行下降方向,新的迭代点由不精确线搜索产生,在较弱的条件下,我们证明了算法的全局收敛性  相似文献
3.
We study a special class of Finsler metrics,namely,Matsumoto metrics F=α2α-β,whereαis a Riemannian metric andβis a 1-form on a manifold M.We prove that F is a(weak)Einstein metric if and only ifαis Ricci flat andβis a parallel 1-form with respect toα.In this case,F is Ricci flat and Berwaldian.As an application,we determine the local structure and prove the 3-dimensional rigidity theorem for a(weak)Einstein Matsumoto metric.  相似文献
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In this paper, we give some results on Laplacian spectral radius of graphs with cut vertices, and as their applications, we also determine the unique graph with the largest Laplacian spectral radius among all unicyclic graphs with n vertices and diameter d, 3?d?n−3.  相似文献
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Let denote the graph obtained by attaching m pendent edges to a vertex of complete graph Kn-m, and Un,p the graph obtained by attaching n-p pendent edges to a vertex of Cp. In this paper, we first prove that the graph and its complement are determined by their adjacency spectra, and by their Laplacian spectra. Then we prove that Un,p is determined by its Laplacian spectrum, as well as its adjacency spectrum if p is odd, and find all its cospectral graphs for Un,4.  相似文献
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In this paper, we study the largest Laplacian spectral radius of the bipartite graphs with n vertices and k cut edges and the bicyclic bipartite graphs, respectively. Identifying the center of a star K1,k and one vertex of degree n of Km,n, we denote by the resulting graph. We show that the graph (1?k?n-4) is the unique graph with the largest Laplacian spectral radius among the bipartite graphs with n vertices and k cut edges, and (n?7) is the unique graph with the largest Laplacian spectral radius among all the bicyclic bipartite graphs.  相似文献
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In the following article, we investigate a particle filter for approximating Feynman–Kac models with indicator potentials and we use this algorithm within Markov chain Monte Carlo (MCMC) to learn static parameters of the model. Examples of such models include approximate Bayesian computation (ABC) posteriors associated with hidden Markov models (HMMs) or rare-event problems. Such models require the use of advanced particle filter or MCMC algorithms to perform estimation. One of the drawbacks of existing particle filters is that they may “collapse,” in that the algorithm may terminate early, due to the indicator potentials. In this article, using a newly developed special case of the locally adaptive particle filter, we use an algorithm that can deal with this latter problem, while introducing a random cost per-time step. In particular, we show how this algorithm can be used within MCMC, using particle MCMC. It is established that, when not taking into account computational time, when the new MCMC algorithm is applied to a simplified model it has a lower asymptotic variance in comparison to a standard particle MCMC algorithm. Numerical examples are presented for ABC approximations of HMMs.  相似文献
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