In the present paper we present a class of polynomial primal-dual interior-point algorithms for semidefmite optimization based on a kernel function. This kernel function is not a so-called self-regular function due to...In the present paper we present a class of polynomial primal-dual interior-point algorithms for semidefmite optimization based on a kernel function. This kernel function is not a so-called self-regular function due to its growth term increasing linearly. Some new analysis tools were developed which can be used to deal with complexity ＂analysis of the algorithms which use analogous strategy in [5] to design the search directions for the Newton system. The complexity bounds for the algorithms with large- and small-update methodswere obtained, namely,O（qn^（p＋q/q（P＋1）log n/ε and O（q^2√n）log n/ε,respectlvely.展开更多
Interior-point methods （IPMs） for linear optimization （LO） and semidefinite optimization （SDO） have become a hot area in mathematical programming in the last decades. In this paper, a new kernel function with si...Interior-point methods （IPMs） for linear optimization （LO） and semidefinite optimization （SDO） have become a hot area in mathematical programming in the last decades. In this paper, a new kernel function with simple algebraic expression is proposed. Based on this kernel function, a primal-dual interior-point methods （IPMs） for semidefinite optimization （SDO） is designed. And the iteration complexity of the algorithm as O（n^3/4 log n/ε） with large-updates is established. The resulting bound is better than the classical kernel function, with its iteration complexity O（n log n/ε） in large-updates case.展开更多
Kernel functions play an important role in defining new search directions for primal-dual interior-point algorithm for solving linear optimization problems. In this paper we present a new kernel function which yields ...Kernel functions play an important role in defining new search directions for primal-dual interior-point algorithm for solving linear optimization problems. In this paper we present a new kernel function which yields an algorithm with the best known complexity bound for both large- and small-update methods.展开更多
进化类算法和内点法交替迭代的混合算法在求解含电压源换流器的高压直流输电(voltage source converter basedhigh voltage direct current,VSC-HVDC)的交直流系统最优潮流(optimal power flow,OPF)问题时由于截断误差的影响和VSC-HVDC...进化类算法和内点法交替迭代的混合算法在求解含电压源换流器的高压直流输电(voltage source converter basedhigh voltage direct current,VSC-HVDC)的交直流系统最优潮流(optimal power flow,OPF)问题时由于截断误差的影响和VSC-HVDC控制方式的限制,容易发生振荡,因此提出一种基于差分进化(differential evolution,DE)和原—对偶内点法(primal-dual interior point method,PDIPM)的统一混合迭代算法。算法的主要思想是以DE算法为框架,对离散变量进行优化,在DE算法的每一次迭代过程中,采用PDIPM对每个DE个体进行连续变量的优化和适应度评估。由于采用PDIPM进行DE种群适应度评估,无需设定VSC-HVDC的控制方式,因此提高了算法的全局寻优能力。多个算例结果表明,该混合算法数值稳定性高,寻优能力强,能很好地解决含两端、多端、多馈入VSC-HVDC的交直流系统最优潮流问题。展开更多
电压源换流器(voltage source converter,VSC)在稳态模型和工作原理上与传统高压直流输电(high voltage directcurrent,HVDC)的换流器有本质区别,因此传统的交直流系统最优潮流计算方法不适用于含基于电压源换流器高压直流输电(VSC base...电压源换流器(voltage source converter,VSC)在稳态模型和工作原理上与传统高压直流输电(high voltage directcurrent,HVDC)的换流器有本质区别,因此传统的交直流系统最优潮流计算方法不适用于含基于电压源换流器高压直流输电(VSC based HVDC,VSC-HVDC)的交直流系统。讨论一种适用于原对偶内点法(primal-dual interior-pointmethod,PDIPM)和预测校正内点法(predictor-corrector PDIPM,PCPDIPM)解最优潮流的VSC-HVDC稳态模型。基于该稳态模型,将VSC-HVDC直流网络与交流系统结合起来,对交直流系统进行联立求解,并对多组算例进行仿真和分析,算例结果表明原对偶内点法在解决含VSC-HVDC的最优潮流问题的能力上,保持了传统内点法最优潮流的高效性,而在同样的条件下,预测–校正内点法迭代次数大大少于原对偶内点法。展开更多
20世纪70年代,电力工作者开始对系统区域间的可用输电能力(Available Transfer Capability,ATC)进行研究。在联邦能源管理委员会(Federal Energy Regulatory Commission,FERC)下达要求输电网的运行单位需计算ATC的命令之后,ATC的研究受...20世纪70年代,电力工作者开始对系统区域间的可用输电能力(Available Transfer Capability,ATC)进行研究。在联邦能源管理委员会(Federal Energy Regulatory Commission,FERC)下达要求输电网的运行单位需计算ATC的命令之后,ATC的研究受到了广泛的关注。快速、可靠地评估ATC对系统的输电可靠性和电力市场交易顺利进行有着重要的作用。基于预测校正内点法计算速度快、鲁棒性好、快速收敛等优点,将预测校正内点法(Predictor-corrector Primal-dual Interior-point Method,PCPDIPM)应用于电力系统ATC计算;通过对模型进行仿真分析,与传统原对偶内点法(Primal-dual Interior-point Method,PDIPM)计算ATC进行比较,验证模型的实用性和算法的有效性及快速收敛性。展开更多
We present a modified and simplified version of an infeasible interior-point method for second-order cone optimization published in 2013(Zangiabadi et al.in J Optim Theory Appl,2013).In the earlier version,each iterat...We present a modified and simplified version of an infeasible interior-point method for second-order cone optimization published in 2013(Zangiabadi et al.in J Optim Theory Appl,2013).In the earlier version,each iteration consisted of one socalled feasibility step and a few centering steps.Here,each iteration consists of only a feasibility step.Thus,the new algorithm improves the number of iterations and the improvement is due to a lemma which gives an upper bound for the proximity after the feasibility step.The complexity result coincides with the best-known iteration bound for infeasible interior-point methods.展开更多
文摘In the present paper we present a class of polynomial primal-dual interior-point algorithms for semidefmite optimization based on a kernel function. This kernel function is not a so-called self-regular function due to its growth term increasing linearly. Some new analysis tools were developed which can be used to deal with complexity ＂analysis of the algorithms which use analogous strategy in [5] to design the search directions for the Newton system. The complexity bounds for the algorithms with large- and small-update methodswere obtained, namely,O（qn^（p＋q/q（P＋1）log n/ε and O（q^2√n）log n/ε,respectlvely.
基金Project supported by the National Natural Science Foundation of China （Grant No. 10117733）, the Shanghai Leading Academic Discipline Project （Grant No.J50101）, and the Foundation of Scientific Research for Selecting and Cultivating Young Excellent University Teachers in Shanghai （Grant No.06XPYQ52）
文摘Interior-point methods （IPMs） for linear optimization （LO） and semidefinite optimization （SDO） have become a hot area in mathematical programming in the last decades. In this paper, a new kernel function with simple algebraic expression is proposed. Based on this kernel function, a primal-dual interior-point methods （IPMs） for semidefinite optimization （SDO） is designed. And the iteration complexity of the algorithm as O（n^3/4 log n/ε） with large-updates is established. The resulting bound is better than the classical kernel function, with its iteration complexity O（n log n/ε） in large-updates case.
基金Supported by National Natural Science Foundation of China (Grant Nos.10771133 and 70871082)Shanghai Leading Academic Discipline Project (Grant No.S30104)
文摘Kernel functions play an important role in defining new search directions for primal-dual interior-point algorithm for solving linear optimization problems. In this paper we present a new kernel function which yields an algorithm with the best known complexity bound for both large- and small-update methods.
文摘进化类算法和内点法交替迭代的混合算法在求解含电压源换流器的高压直流输电(voltage source converter basedhigh voltage direct current,VSC-HVDC)的交直流系统最优潮流(optimal power flow,OPF)问题时由于截断误差的影响和VSC-HVDC控制方式的限制,容易发生振荡,因此提出一种基于差分进化(differential evolution,DE)和原—对偶内点法(primal-dual interior point method,PDIPM)的统一混合迭代算法。算法的主要思想是以DE算法为框架,对离散变量进行优化,在DE算法的每一次迭代过程中,采用PDIPM对每个DE个体进行连续变量的优化和适应度评估。由于采用PDIPM进行DE种群适应度评估,无需设定VSC-HVDC的控制方式,因此提高了算法的全局寻优能力。多个算例结果表明,该混合算法数值稳定性高,寻优能力强,能很好地解决含两端、多端、多馈入VSC-HVDC的交直流系统最优潮流问题。
文摘电压源换流器(voltage source converter,VSC)在稳态模型和工作原理上与传统高压直流输电(high voltage directcurrent,HVDC)的换流器有本质区别,因此传统的交直流系统最优潮流计算方法不适用于含基于电压源换流器高压直流输电(VSC based HVDC,VSC-HVDC)的交直流系统。讨论一种适用于原对偶内点法(primal-dual interior-pointmethod,PDIPM)和预测校正内点法(predictor-corrector PDIPM,PCPDIPM)解最优潮流的VSC-HVDC稳态模型。基于该稳态模型,将VSC-HVDC直流网络与交流系统结合起来,对交直流系统进行联立求解,并对多组算例进行仿真和分析,算例结果表明原对偶内点法在解决含VSC-HVDC的最优潮流问题的能力上,保持了传统内点法最优潮流的高效性,而在同样的条件下,预测–校正内点法迭代次数大大少于原对偶内点法。
文摘We present a modified and simplified version of an infeasible interior-point method for second-order cone optimization published in 2013(Zangiabadi et al.in J Optim Theory Appl,2013).In the earlier version,each iteration consisted of one socalled feasibility step and a few centering steps.Here,each iteration consists of only a feasibility step.Thus,the new algorithm improves the number of iterations and the improvement is due to a lemma which gives an upper bound for the proximity after the feasibility step.The complexity result coincides with the best-known iteration bound for infeasible interior-point methods.
