线性l1模极小化问题的熵函数延拓法

本文通过利用极大熵函数构造同伦映射，建立了求解无约束线性l1模问题的熵函数延拓算法，证明了方法的收敛性，并给出了数值算例．     

一类二层优化问题的极大熵方法

文章对一类下层带有公差的特殊的二层优化问题构造出不同于文[1]-[4]的极大熵函数来近似表示下层极值函数,地不可微二层优化问题转化为可微优化问题来处理，从而得到一类二层优化问题的ε－最优解的一种计算方法。     

非线性l_1问题的极大熵方法

本文给出求解非线性l1问题的极大熵方法．介绍了极大熵函数的性质,极大熵算法及其收敛性,最后给出一个算例。     

非线性l1问题的极大熵方法

本文给出求解非线性l1问题的极大熵方法,介绍了极大熵函数的性质,极大熵算法及其收敛性。最后给出一个算例。     

基于冗余紧框架的?2/?1极小化块稀疏压缩感知

压缩感知是(近似)稀疏信号处理的研究热点之一,它突破了Nyquist/Shannon采样率,实现了信号的高效采集和鲁棒重构.本文采用l2/l1极小化方法和BlockD-RIP理论研究了在冗余紧框架下的块稀疏信号,所获结果表明,当BlockD-RIP常数δ2k/τ满足0<δ2k/τ<0.2时,l2/l1极小化方法能够鲁棒重构原始信号,同时改进了已有的重构条件和误差上界.基于离散傅里叶变换(DFT)字典,执行了一系列仿真实验充分证实了理论结果.     

半无限极大极小问题的极大熵方法

给出了一种求解半无限极大极小问题的极大熵方法，其基本思想是将半无限极大极小问题用有限维的可微无约束优化问题来近似．研究了方法的一些性质，并证明了方法的收敛性．文末的数值结果说明：这种方法是可行的，而算法的构造比已知的算法要容易得多，因而易于在工程设计中推广应用．     

摩擦市场中最优证券组合选择的极大极小法

在考虑交易成本的基础上,构造最优投资组合选择的极大极小模型,同时允许投资者卖空风险资产.在求解过程中,针对出现的非光滑函数,通过引入极大熵函数用光滑问题来逼近非光滑问题.最后推导出连续可微的方程组,可采用经典牛顿法求解.数值分析验证了该方法的有效性.     

PSBH中的组合优化问题及其计算方法

本文介绍了具有部分位置信息的SBH杂交测序（Positional Sequencing by Hy-bridization,简称PSBH）实验所产生的一个重构DNA片断的组合优化问题,并讨论了该问题最优重构的计算问题.通过对PSBH提供的谱集及其位置信息的分析处理,我们获得了若干判定最优重构片断头尾的分支定界准则以及确定其非头尾位置最可能出现k-tuple的动态规划计算方法,并由此给出了该PSBH问题的一个新重构算法.该算法允许PSBH谱集含有一般杂交实验中常常可能出现探针错配所产生的正错误,并且仅仅假设PSBH的谱集、位置信息和位置长度是已知的,所以我们的算法具有更一般的适应性和实用性.此外,由于我们给出的算法能够极大地利用PSBH的谱集和位置信息所蕴含的信息确定最优重构片断头尾及其中间位置最可能出现的k-tuple,极大地减少了PSBH重构中的随意性,所以我们的算法也是有效的,模拟PSBH实验的计算结果验证了这一点.     

求解线性规划的极大熵方法

极大熵方法是求解多约束非线性规划和极大极小问题的一种有效的方法.用它来求解多约束优化问题,一种途径是将多约束用单约束近似,再用增广Lagrange乘子法求解近似问题;另一种途径是用极大熵方法构造精确罚函数的近似.无论是哪一种途径都需要估计乘子的上界.能否构造不引入乘子估计的算法是很有意义的.Karmarkar算法是求解线性规划的一种有效的多项式内点方法.这种方法在每一次迭代时都要作变换,在像空间用内切球近似单纯形的近似问题得到像空间的新的近似解,再作逆变换求得原空间的新的近似解.可见一次性地构造近似问题并求解之而得     

参变极值问题的信息凝聚分布与Boltzmann极大熵函数

该文利用Ｂｏｌｔｚｍａｎｎ 熵概念给出了参变极值问题最优解的一种积分极限表达式和极值函数的极大熵函数,讨论了它们一致收敛性的要求并给出了极大熵函数一致收敛的一个充分条件,将之应用到全局最优解问题得到了全局最优解和最优值的一种显表示,最后还探讨了极大熵函数在一类双层规划问题求解中的应用．     

基于l1-l2范数的块稀疏信号重构

压缩感知(compressed sensing,CS) 是一种全新的信息采集与处理的理论框架,借助信号内在的稀疏性或可压缩性,可以从小规模的线性、非自适应的测量中通过求解非线性优化问题重构原信号.块稀疏信号是一种具有块结构的信号,即信号的非零元是成块出现的.受YIN Peng-hang, LOU Yi-fei, HE Qi等提出的l_1-2范数最小化方法的启发,将基于l₁-l₂范数的稀疏重构算法推广到块稀疏模型,证明了块稀疏模型下l₁-l₂范数的相关性质,建立了基于l₁-l₂范数的块稀疏信号精确重构的充分条件,并通过DCA(difference of convex functions algorithm) 和ADMM(alternating direction method of multipliers)给出了求解块稀疏模型下l₁-l₂范数的迭代方法.数值实验表明,基于l₁-l₂范数的块稀疏重构算法比其他块稀疏重构算法具有更高的重构成功率.     

一类不可微二次规划逆问题

本文求解了一类二次规划的逆问题,具体为目标函数是矩阵谱范数与向量无穷范数之和的最小化问题.首先将该问题转化为目标函数可分离变量的凸优化问题,提出用G-ADMM法求解.并结合奇异值阈值算法,Moreau-Yosida正则化算法,matlab优化工具箱的quadprog函数来精确求解相应的子问题.而对于其中一个子问题的精确求解过程中发现其仍是目标函数可分离变量的凸优化问题,由于其变量都是矩阵,所以采用适合多个矩阵变量的交替方向法求解,通过引入新的变量,使其每个子问题的解都具有显示表达式.最后给出采用的G-ADMM法求解本文问题的数值实验.数据表明,本文所采用的方法能够高效快速地解决该二次规划逆问题.     

城市道路交通信号实时控制的数学模型与算法研究

研究了交通信号的实时配时控制问题.建立了在已有交通设施条件下,控制信号具有线性约束的非线性实时配时系统优化模型,设计了与模型相适应的实时CLY系列算法.重点讨论了点控制问题,建立了相应的数学优化模型,设计了CLY-Point1算法求解.还对线控制问题和面控制问题,建立了多层优化控制模型,并设计CLY-Point2、CLY-Line和CLY-Area算法进行求解.数值模拟结果表明,CLY系列算法具有很强的实时性,车辆平均等待时间比固定配时减少了约20%.     

A new model for sparse and low-rank matrix decomposition

The robust principal component analysis (RPCA) model is a popular method for solving problems with the nuclear norm and $\ell_1$ norm. However, it is time-consuming since in general one has to use the singular value decomposition in each iteration. In this paper, we introduce a novel model to reformulate the existed model by making use of low-rank matrix factorization to surrogate the nuclear norm for the sparse and low-rank decomposition problem. In such case we apply the Penalty Function Method (PFM) and Augmented Lagrangian Multipliers Method (ALMM) to solve this new non-convex optimization problem. Theoretically, corresponding to our methods, the convergence analysis is given respectively. Compared with classical RPCA, some practical numerical examples are simulated to show that our methods are much better than RPCA.     

ERROR ESTIMATES FOR SPARSE OPTIMAL CONTROL PROBLEMS BY PIECEWISE LINEAR FINITE ELEMENT APPROXIMATION

Optimization problems with L¹-control cost functional subject to an elliptic partial differential equation(PDE)are considered.However,different from the finite dimensiona l¹-regularization optimization,the resulting discretized L¹norm does not have a decoupled form when the standard piecewise linear finite element is employed to discretize the continuous problem.A common approach to overcome this difficulty is employing a nodal quadrature formula to approximately discretize the L¹-norm.In this paper,a new discretized scheme for the L¹-norm is presented.Compared to the new discretized scheme for L¹-norm with the nodal quadrature formula,the advantages of our new discretized scheme can be demonstrated in terms of the order of approximation.Moreover,finite element error estimates results for the primal problem with the new discretized scheme for the L¹-norm are provided,which confirms that this approximation scheme will not change the order of error estimates.To solve the new discretized problem,a symmetric Gauss-Seidel based majorized accelerated block coordinate descent(sGS-mABCD)method is introduced to solve it via its dual.The proposed sGS-mABCD algorithm is illustrated at two numerical examples.Numerical results not only confirm the finite element error estimates,but also show that our proposed algorithm is efficient.     

1-Bit compressive sensing: Reformulation and RRSP-based sign recovery theory

Recently, the 1-bit compressive sensing(1-bit CS) has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption(that has been overlooked in the literature) should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an ?_0-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property(RRSP) of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a k-sparse signal can be exactly recovered with 1-bit basis pursuit.     

Convex feasibility modeling and projection methods for sparse signal recovery

A computationally-efficient method for recovering sparse signals from a series of noisy observations, known as the problem of compressed sensing (CS), is presented. The theory of CS usually leads to a constrained convex minimization problem. In this work, an alternative outlook is proposed. Instead of solving the CS problem as an optimization problem, it is suggested to transform the optimization problem into a convex feasibility problem (CFP), and solve it using feasibility-seeking sequential and simultaneous subgradient projection methods, which are iterative, fast, robust and convergent schemes for solving CFPs. As opposed to some of the commonly-used CS algorithms, such as Bayesian CS and Gradient Projections for sparse reconstruction, which become inefficient as the problem dimension and sparseness degree increase, the proposed methods exhibit robustness with respect to these parameters. Moreover, it is shown that the CFP-based projection methods are superior to some of the state-of-the-art methods in recovering the signal’s support. Numerical experiments show that the CFP-based projection methods are viable for solving large-scale CS problems with compressible signals.     

Inversion algorithm based on the generalized objective functional for compressed sensing

Owing to providing a novel insight for signal and image processing, compressed sensing (CS) has attracted increasing attention. The accuracy of the reconstruction algorithms plays an important role in real applications of the CS theory. In this paper, a generalized reconstruction model that simultaneously considers the inaccuracies on the measurement matrix and the measurement data is proposed for CS reconstruction. A generalized objective functional, which integrates the advantages of the least squares (LSs) estimation and the combinational M-estimation, is proposed. An iterative scheme that integrates the merits of the homotopy method and the artificial physics optimization (APO) algorithm is developed for solving the proposed objective functional. Numerical simulations are implemented to evaluate the feasibility and effectiveness of the proposed algorithm. For the cases simulated in this paper, the reconstruction accuracy is improved, which indicates that the proposed algorithm is successful in solving CS inverse problems.     

Properties and methods for finding the best rank-one approximation to higher-order tensors

The problem of finding the best rank-one approximation to higher-order tensors has extensive engineering and statistical applications. It is well-known that this problem is equivalent to a homogeneous polynomial optimization problem. In this paper, we study theoretical results and numerical methods of this problem, particularly focusing on the 4-th order symmetric tensor case. First, we reformulate the polynomial optimization problem to a matrix programming, and show the equivalence between these two problems. Then, we prove that there is no duality gap between the reformulation and its Lagrangian dual problem. Concerning the approaches to deal with the problem, we propose two relaxed models. The first one is a convex quadratic matrix optimization problem regularized by the nuclear norm, while the second one is a quadratic matrix programming regularized by a truncated nuclear norm, which is a D.C. function and therefore is nonconvex. To overcome the difficulty of solving this nonconvex problem, we approximate the nonconvex penalty by a convex term. We propose to use the proximal augmented Lagrangian method to solve these two relaxed models. In order to obtain a global solution, we propose an alternating least eigenvalue method after solving the relaxed models and prove its convergence. Numerical results presented in the last demonstrate, especially for nonpositive tensors, the effectiveness and efficiency of our proposed methods.     

Properties and Iterative Methods for the Lasso and Its Variants

The lasso of Tibshirani （1996） is a least-squares problem regularized by the l1 norm. Due to the sparseness promoting property of the l1 norm, the lasso has been received much attention in recent years. In this paper some basic properties of the lasso and two variants of it are exploited. Moreover, the proximal method and its variants such as the relaxed proximal algorithm and a dual method for solving the lasso by iterative algorithms are presented.