基于基底合并的分片稀疏恢复

如我们所知,诸如视频和图像等信号可以在某些框架下被表示为稀疏信号,因此稀疏恢复(或稀疏表示)是信号处理、图像处理、计算机视觉、机器学习等领域中被广泛研究的问题之一.通常大多数在稀疏恢复中的有效快速算法都是基于求解$l^0$或者$l^1$优化问题.但是,对于求解$l^0$或者$l^1$优化问题以及相关算法所得到的理论充分性条件对信号的稀疏性要求过严.考虑到在很多实际应用中,信号是具有一定结构的,也即,信号的非零元素具有一定的分布特点.在本文中,我们研究分片稀疏恢复的唯一性条件和可行性条件.分片稀疏性是指一个稀疏信号由多个稀疏的子信号合并所得.相应的采样矩阵是由多个基底合并组成.考虑到采样矩阵的分块结构,我们引入了子矩阵的互相干性,由此可以得到相应$l^0$或者$l^1$优化问题可精确恢复解的稀疏度的新上界.本文结果表明.通过引入采样矩阵的分块结构信息.可以改进分片稀疏恢复的充分性条件.以及相应$l^0$或者$l^1$优化问题整体稀疏解的可靠性条件.     

求解社区项目博弈的一种改进贪婪算法研究

针对社区项目博弈的一般模型,应用贪婪算法求解项目博弈的近似社会最优指派,给出参与者重加权分配机制,证明贪婪算法求得的指派恰好是非合作项目博弈的一个纳什均衡.定义控制参数,给出边际效益后悔值定义,利用后悔值改进了贪婪算法,证明基于后悔值贪婪算法求得的指派是非合作项目博弈的一个纳什均衡.通过数值仿真实验发现,与模拟退火算法比较,贪婪算法能够得到更好的社会效益,而且基于后悔值贪婪算法比贪婪算法得到更好的社会效益.     

矩阵广义逆硬阈值追踪算法与稀疏恢复问题

本文通过引入支撑集捕获基数及MP广义逆,提出了一种用于稀疏恢复问题的矩阵广义逆硬阈值追踪算法,并在观测误差存在的情况下给出了算法在约束等距条件(RIP)下的收敛性.数值实验表明,算法不仅极大地减少了收敛所需迭代次数,且观测误差存在的情况下稀疏恢复是强健的.     

求解稀疏相位恢复问题的随机交替方向法(英文)

近年来稀疏相位恢复问题受到了越来越多的关注.本文提出了一种随机交替方法方法求解稀疏相位恢复问题,该算法采用硬阈值追踪算法求解带稀疏约束的最小二乘子问题.大量的数值实验表明,该算法可以通过O(s log n)次测量(理论上最少测量值)稳定的恢复n维s稀疏向量,并且在随机初值下可以获得全局收敛性.     

求解多维约束下下模函数最大值的改进贪婪算法

提出了多维约束下下模函数最大值问题,分析其在组合优化中的重要应用.此问题是NP-难的,故给出了求解该问题的改进贪婪算法.最后,从理论上证明了这一算法的时间复杂性和性能保证.说明该算法是多项式时间近似算法,同时也具有较好的性能保证.     

求解稀疏逻辑回归问题的嵌套BB算法的分裂增广拉格朗日算法

逻辑回归是经典的分类方法,广泛应用于数据挖掘、机器学习和计算机视觉.现研究带有程。模约束的逻辑回归问题.这类问题广泛用于分类问题中的特征提取,且一般是NP-难的.为了求解这类问题,提出了嵌套BB(Barzilai and Borwein)算法的分裂增广拉格朗日算法(SALM-BB).该算法在迭代中交替地求解一个无约束凸优化问题和一个带程。模约束的二次优化问题.然后借助BB算法求解无约束凸优化问题.通过简单的等价变形直接得到带程。模约束二次优化问题的精确解,并且给出了算法的收敛性定理.最后通过数值实验来测试SALM-BB算法对稀疏逻辑回归问题的计算精确性.数据来源包括真实的UCI数据和模拟数据.数值实验表明,相对于一阶算法SLEP,SALM-BB能够得到更低的平均逻辑损失和错分率.     

一类稀疏约束下不适定反问题的求解

矩阵填充是一类稀疏先验下的不适定的反问题.首先阐述了矩阵填充的基本原理,指出只有当待求的矩阵满足不相关特性或秩-受限等距特性时,才有可能精确重构未知矩阵.Jain P等将矩阵的不相关特性与秩-受限等距特性联系起来,提出了仿射-受限等距特性,但没有说明秩-受限等距常数与不相关常数的关系,定理3给出了两者之间的的数值关系,有效的促进了矩阵填充的研究.接下来分析了矩阵填充中常用的几种算法,并在随后的仿真实验中对这几种算法的重构性能做了详细比较.     

一类带时间窗车辆分配问题的贪婪算法

本文对一类带时间窗的车辆分配问题进行了分析,引入了车辆任务的概念,并将问题转化为车辆与车辆任务的匹配问题,同时制订了运输任务选择和车辆选择的贪婪策略,并在此基础上设计了车辆分配问题的贪婪算法,最后通过实例验证了算法的有效性。     

快件揽收实时车辆路径问题的一种贪婪算法

快递运营中，调派车辆前往随机发生的快件发件人处上门揽收快件，是一个实时编排行车路径的动态决策过程．本文针对该问题，采用了揽收所有快件的最后时刻最早和行车路径最短的目标，结合车辆揽收快件数平衡的要求，给出一种贪婪算法；然后，对Solomon设计的100个点规模的VRPTW算例做计算试验，分析了车辆数对目标的影响．     

Besov类上的贪婪算法

我们讨论了Besov类MBpr,θ上的相应于张量积小波词典Wd的最佳m-项 逼近问题,证明了其最佳m-项逼近的阶可以通过简单的贪婪算法得到.     

Variant of Greedy Randomized Gauss-Seidel Method for Ridge Regression

The variants of randomized Kaczmarz and randomized Gauss-Seidel algorithms are two effective stochastic iterative methods for solving ridge regression problems. For solving ordinary least squares regression problems, the greedy randomized Gauss-Seidel (GRGS) algorithm always performs better than the randomized Gauss-Seidel algorithm (RGS) when the system is overdetermined. In this paper, inspired by the greedy modification technique of the GRGS algorithm, we extend the variant of the randomized Gauss-Seidel algorithm, obtaining a variant of greedy randomized Gauss-Seidel (VGRGS) algorithm for solving ridge regression problems. In addition, we propose a relaxed VGRGS algorithm and the corresponding convergence theorem is established. Numerical experiments show that our algorithms outperform the VRK-type and the VRGS algorithms when $m > n$.     

Greedy maximal independent sets via local limits

The random greedy algorithm for finding a maximal independent set in a graph constructs a maximal independent set by inspecting the graph's vertices in a random order, adding the current vertex to the independent set if it is not adjacent to any previously added vertex. In this paper, we present a general framework for computing the asymptotic density of the random greedy independent set for sequences of (possibly random) graphs by employing a notion of local convergence. We use this framework to give straightforward proofs for results on previously studied families of graphs, like paths and binomial random graphs, and to study new ones, like random trees and sparse random planar graphs. We conclude by analysing the random greedy algorithm more closely when the base graph is a tree.     

Greedy expansions in Banach spaces

We study convergence and rate of convergence of expansions of elements in a Banach space X into series with regard to a given dictionary . For convenience we assume that is symmetric: implies . The primary goal of this paper is to study representations of an element f∈X by a series

Reconstruction of Sparse Polynomials via Quasi-Orthogonal Matching Pursuit Method

In this paper, we propose a Quasi-Orthogonal Matching Pursuit (QOMP) algorithm for constructing a sparse approximation of functions in terms of expansion by orthonormal polynomials. For the two kinds of sampled data, data with noises and without noises, we apply the mutual coherence of measurement matrix to establish the convergence of the QOMP algorithm which can reconstruct $s$-sparse Legendre polynomials, Chebyshev polynomials and trigonometric polynomials in $s$ step iterations. The results are also extended to general bounded orthogonal system including tensor product of these three univariate orthogonal polynomials. Finally, numerical experiments will be presented to verify the effectiveness of the QOMP method.     

贪婪t－相交有限序列族

设Ｆ为有限序列族，对ａ＝（ａ１，ａ２，…，ａｎ）∈Ｆ，ａｉ为整数且０≤ａｉ≤ｓｉ（整数），记ｓ（ａ）＝｛ｊ｜１≤ｊ≤ｎ，ａｊ＞０｝，ｓ（Ｆ）＝｛ｓ（ａ）｜ａ∈Ｆ｝，及Ａ｛１，２，…，ｎ｝时Ｗ（Ａ）＝Пｉ∈Ａｓｉ．称Ｆ为贪婪ｔ－相交，如对任何ａ，ｂ∈Ｆ，至少有ｔ个ａｉ，ｂｉ＞０，且Ｗ（Ａ）≥Ｗ（（｛１，２，…，ｎ｝－Ａ）＋Ｂ）对任何Ａ∈Ｓ（Ｆ）及ＢＡ（｜Ｂ｜＝ｔ－１）成立．本文得到当ｓ１＞ｓ２＞…＞ｓｎ时的最大贪婪ｔ－相交有限序列族．     

Approximate Weak Greedy Algorithms

We present a generalization of Temlyakov's weak greedy algorithm, and give a sufficient condition for norm convergence of the algorithm for an arbitrary dictionary in a Hilbert space. We provide two counter-examples to show that the condition cannot be relaxed for general dictionaries. For a class of dictionaries with more structure, we give a more relaxed necessary and sufficient condition for convergence of the algorithm. We also provide a detailed discussion of how a real-world implementation of the weak greedy algorithm, where one has to take into account floating point arithmetic and other types of finite precision errors, can be modeled by the new algorithm.     

加速修改AHP中的判断矩阵的贪婪算法

通过分析判断矩阵 ,一致性矩阵 ,导出矩阵及度量矩阵的关系 ,提出一种修改判断矩阵的预测加速修正的贪婪算法 .贪婪法不追求最优解 ,不要回溯 ,只希望得到较为满意的解 .当判断矩阵的一致性较差时 ,基于度量矩阵中偏离大的元素对判断矩阵一致性的影响较大 ,通过导出矩阵和度量矩阵得出加速修正的步长 .每次只修改判断矩阵的一对元素 .实例分析表明 ,修改 AHP中的判断矩阵的贪婪算法是可行的 .     

Greedy colorings of uniform hypergraphs

We give a very short proof of an Erd?s conjecture that the number of edges in a non‐2‐colorable n‐uniform hypergraph is at least f(n)2ⁿ, where f(n) goes to infinity. Originally it was solved by József Beck in 1977, showing that f(n) at least clog n. With an ingenious recoloring idea he later proved that f(n) ≥ cn^1/3+o(1). Here we prove a weaker bound on f(n), namely f(n) ≥ cn^1/4. Instead of recoloring a random coloring, we take the ground set in random order and use a greedy algorithm to color. The same technique works for getting bounds on k‐colorability. It is also possible to combine this idea with the Lovász Local Lemma, reproving some known results for sparse hypergraphs (e.g., the n‐uniform, n‐regular hypergraphs are 2‐colorable if n ≥ 8). © 2009 Wiley Periodicals, Inc. Random Struct. Alg., 2009     

Generalized Approximate Weak Greedy Algorithms

We study generalized approximate weak greedy algorithms. The main difference of these algorithms from approximate weak greedy algorithms proposed by R. Gribonval and M. Nielsen consists in that errors in the calculation of the coefficients can be prescribed in terms of not only their relative values, but also their absolute values. We present conditions on the parameters of generalized approximate weak greedy algorithms which are sufficient for the expansions resulting from the use of this algorithm to converge to the expanded element. It is shown that these conditions cannot be essentially weakened. We also study some questions of the convergence of generalized approximate weak greedy expansions with respect to orthonormal systems.__________Translated from Matematicheskie Zametki, vol. 78, no. 2, 2005, pp. 186–201.Original Russian Text Copyright © 2005 by V. V. Galatenko, E. D. Livshits.