KKT-based iterative algorithm for beamforming in cognitive radio system

In this paper,we consider the downlink channel of multi-user multi-input single-output(MU-MISO)system in cognitive radio network,where the cognitive base station(CBS)resort to beamforming scheme to relief co-channel interference.The design criterion is to minimize the transmit power at CBS,subject to the signal-to-interference-plus-noise-ratio(SINR)constraints of cognitive users(CUs)and the interference constraints at primary users(PUs).Standard conic optimization packages can handle the problem,however,the...     

改进L∞优化算法用于三维空间点快速精确重建

多视几何中的多种问题可以通过最小化L_∞范数误差获得全局最优解。但最小化L_∞范数误差算法的缺点是对外点敏感,相关的改进算法虽然可以克服外点带来的影响,但计算速度较慢。提出一种改进的最小化L_∞范数误差算法,用于从包含外点的图像序列中快速精确重建三维空间点。真实测试图像的实验结果证明该算法可以在包含外点的情况下获得空间点的全局最优解,相比其他算法速度有较大的提高。     

一种二阶锥规划通带线性相位FIR滤波器的设计

在有限冲激响应（Finite Impulse Response,FIR）滤波器设计中,如果系统只要求通带或某个频域区间具有线性相位而其他频域区间相位非线性,则系数对称的FIR滤波器设计方法不再适用。为此,提出了一种基于二阶锥规划（Second-Order Cone Programming,SOCP）的通带线性相位FIR滤波器设计方法。该方法使用二阶锥规划实现滤波器设计,其中优化目标为通带最小群延迟,约束条件为全频域振幅误差。实验结果显示,所提方法设计的FIR滤波器有着很好的幅频特性和通带线性相位,通带群延迟误差很小。该方法实现简单,计算复杂度低,可以广泛应用于数字信号处理领域。     

基于迭代二阶锥的唯相位波束形成

唯相位自适应波束形成技术对普通相控阵雷达自适应干扰抑制是非常重要的,在数字阵列雷达中,唯相位技术则可以充分利用各阵元发射模块微波功率从而提高雷达的威力。该文提出了一种新的唯相位波束形成方法,假定初始唯相位权重矢量有个较小的相位扰动,将唯相位模型的目标函数和约束函数在该相位扰动区域内分别用泰勒一阶展开式来近似,则可以将原来的非凸问题转化为凸优化问题,通过二阶锥规划方法(SOCP)求得使当前目标函数最小的扰动矢量,然后更新得到新的权重矢量并代替原来的权重矢量,重复上述迭代过程直到满足停止条件,可以得到满足要求的唯相位权重。计算机仿真结果验证了该方法的正确性和有效性。     

计及VSC和DFIG的电网动态无功优化模型

近年来,一系列特高压直流工程的投运和大型风电场等新能源的并网给特高压近区电网无功控制带来了新的挑战。为解决含柔性直流的交直流混联电网的无功优化控制问题,提出了含电压源换流器(Voltage Source Converter, VSC)并考虑双馈感应风机(Doubly-Fed Induction Generator, DFIG)无功支撑的动态无功优化模型。该模型以交直流电网全天网损最小为目标函数,约束包括交直流系统的潮流约束、直流变量的控制约束、离散控制变量动态调节次数约束及节点电压的安全约束。原模型是一个多时段非线性混合整数规划问题,缺乏快速有效的求解方法,通过线性化技术将原模型转化为能有效求解的二阶锥规划(Second Order Cone Programming, SOCP)问题。以IEEE30节点系统为例,通过仿真计算验证了所建模型和算法的有效性。     

Large margin classification for combating disguise attacks on spam filters

This paper addresses the challenge of large margin classification for spam filtering in the presence of an adversary who disguises the spam mails to avoid being detected. In practice, the adversary may strategically add good words indicative of a legitimate message or remove bad words indicative of spam. We assume that the adversary could afiord to modify a spam message only to a certain extent, without damaging its utility for the spammer. Under this assumption, we present a large margin approach for classification of spam messages that may be disguised. The proposed classifier is formulated as a second-order cone programming optimization. We performed a group of experiments using the TREC 2006 Spam Corpus. Results showed that the performance of the standard support vector machine (SVM) degrades rapidly when more words are injected or removed by the adversary, while the proposed approach is more stable under the disguise attack.     

鲁棒多目标线性规划模型及混合遗传算法

针对目标函数系数和约束条件系数均在椭球扰动集下的不确定多目标线性规划, 提出了椭球扰动集下的鲁棒多目标线性规划问题。基于每个目标均需获得鲁棒解的假设下给出了定理及证明, 以此把原问题转换为具有二阶锥约束的确定性多目标优化问题。设计了一种混合策略求解算法, 整体流程采用多目标遗传算法, 局部采用SOCP优化软件Sedumi进行计算, 从而获得不确定多目标线性规划的鲁棒解集, 并通过数值算例验证了该算法的有效性。     

一种基于二阶锥规划的浮点分数时延滤波器

宽带图像声纳通常采用分数时延滤波器来提高时延精度.传统的分数时延滤波器基本采用Farrow定点结构,但其动态范围窄并且需要占用较多的硬件资源.为克服上述不足,提出了一种采用二阶锥规划（SOCP）算法进行分数时延滤波器系数设计,并以分布式算法为基础的浮点分布式时延滤波器.该滤波器具有计算精度高、动态范围大等优点,适于在FPGA上进行规模化应用.     

Recurrent neural networks for solving second-order cone programs

This paper proposes using the neural networks to efficiently solve the second-order cone programs (SOCP). To establish the neural networks, the SOCP is first reformulated as a second-order cone complementarity problem (SOCCP) with the Karush-Kuhn-Tucker conditions of the SOCP. The SOCCP functions, which transform the SOCCP into a set of nonlinear equations, are then utilized to design the neural networks. We propose two kinds of neural networks with the different SOCCP functions. The first neural network uses the Fischer-Burmeister function to achieve an unconstrained minimization with a merit function. We show that the merit function is a Lyapunov function and this neural network is asymptotically stable. The second neural network utilizes the natural residual function with the cone projection function to achieve low computation complexity. It is shown to be Lyapunov stable and converges globally to an optimal solution under some condition. The SOCP simulation results demonstrate the effectiveness of the proposed neural networks.     

计及分时电价的电缆配电网多时段二阶段有功与无功协调快速鲁棒优化调度方法

同时计及分时电价、经逆变器接入配电网的分布式电源有功与无功调节能力、调节设备动作代价及电缆对地分布电容电流,以购电与调节设备动作总费用最小为目标函数,考虑运行约束,基于支路潮流方程构建电缆配电网有功、无功多时段二阶段混合整数二阶锥鲁棒优化模型,并提出一种基于割平面的主、次问题交替迭代求解方法。不同于现有列与约束生成算法,利用所提方法求解主问题时无需增加新的变量与约束条件,利用所提方法求解次问题时只需针对每个时段进行求解,极大降低了求解复杂度与所需计算机内存。4节点、IEEE 33节点与P&G 69节点配电网算例仿真结果验证了所提方法的性能。