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1.
A globally and superlinearly convergent trust region method for LC
1 optimization problems 总被引:1,自引:0,他引:1
ZhangLiping LaiYanlian 《高校应用数学学报(英文版)》2001,16(1):72-80
Abstract. A new trust region algorithm for solving convex LC1 optimization problem is present-ed. It is proved that the algorithm is globally convergent and the rate of convergence is superlin-ear under some reasonable assumptions. 相似文献
2.
In this paper the Pareto efficiency of a uniformly convergent multiobjective optimization sequence is studied. We obtain some relation between the Pareto efficient solutions of a given multiobjective optimization problem and those of its uniformly convergent optimization sequence and also some relation between the weak Pareto efficient solutions of the same optimization problem and those of its uniformly convergent optimization sequence. Besides, under a compact convex assumption for constraints set and a certain convex assumption for both objective and constraint functions, we also get some sufficient and necessary conditions that the limit of solutions of a uniformly convergent multiobjective optimization sequence is the solution of a given multiobjective optimization problem. 相似文献
3.
In this paper,we consider the extremal problem of the lp-norm:min {lp(TK),o∈TK■L,T∈GL(n)},where K,L are two convex bodies in Rn.Using the optimization theorem of John,we give necessary conditions for K to be in extremal position in terms of a decomposition of the identity.Furthermore,the weaker optimization problem,min{(lp(TK))p:TK■B2n,TK∩Sn-1≠,T∈GL(n)},is also considered.As an application,the geometric distance between the unit ball B2n and a centrally symmetric convex body K is obtained. 相似文献
4.
In general normed spaces,we consider a multiobjective piecewise linear optimization problem with the ordering cone being convex and having a nonempty interior.We establish that the weak Pareto optimal solution set of such a problem is the union of finitely many polyhedra and that this set is also arcwise connected under the cone convexity assumption of the objective function.Moreover,we provide necessary and suffcient conditions about the existence of weak(sharp) Pareto solutions. 相似文献
5.
In this work,we present a new method for convex shape representation,which is regardless of the dimension of the concerned objects,using level-set approaches.To the best of our knowledge,the proposed prior is the first one which can work for high dimensional objects.Convexity prior is very useful for object completion in computer vision.It is a very challenging task to represent high dimensional convex objects.In this paper,we first prove that the convexity of the considered object is equivalent to the convexity of the associated signed distance function.Then,the second order condition of convex functions is used to characterize the shape convexity equivalently.We apply this new method to two applications:object segmentation with convexity prior and convex hull problem(especially with outliers).For both applications,the involved problems can be written as a general optimization problem with three constraints.An algorithm based on the alternating direction method of multipliers is presented for the optimization problem.Numerical experiments are conducted to verify the effectiveness of the proposed representation method and algorithm. 相似文献
6.
Ju-liangZhang JianChen Xin-jianZhuo 《计算数学(英文版)》2004,22(4):509-522
In this paper, LCP is converted to an equivalent nonsmooth nonlinear equation system H(x,y) = 0 by using the famous NCP function-Fischer-Burmeister function. Note that some equations in H(x, y) = 0 are nonsmooth and nonlinear hence difficult to solve while the others are linear hence easy to solve. Then we further convert the nonlinear equation system H(x, y) = 0 to an optimization problem with linear equality constraints. After that we study the conditions under which the K-T points of the optimization problem are the solutions of the original LCP and propose a method to solve the optimization problem. In this algorithm, the search direction is obtained by solving a strict convex programming at each iterative point, However, our algorithm is essentially different from traditional SQP method. The global convergence of the method is proved under mild conditions. In addition, we can prove that the algorithm is convergent superlinearly under the conditions: M is P0 matrix and the limit point is a strict complementarity solution of LCP. Preliminary numerical experiments are reported with this method. 相似文献
7.
In this paper, we consider a general composite convex optimization problem with a cone-convex system in locally convex Hausdorff topological vector spaces. Some Fenchel conjugate transforms for the composite convex functions are derived to obtain the equivalent condition of the Stable Farkas Lemma, which is formulated by using the epigraph of the conjugates for the convex functions involved and turns out to be weaker than the classic Slater condition. Moreover, we get some necessary and sufficient conditions for stable duality results of the composite convex functions and present an example to illustrate that the monotonic increasing property of the outer convex function in the objective function is essential. Our main results in this paper develop some recently results. 相似文献
8.
Wen Song Bo-ying Wu Jian-mei Zhang 《应用数学学报(英文版)》2007,23(1):91-98
In this note,we prove that the efficient solution set for a vector optimization problem with acontinuous,star cone-quasiconvex objective mapping is connected under the assumption that the ordering coneis a D-cone.A D-cone includes any closed convex pointed cones in a normed space which admits strictly positivecontinuous linear functionals. 相似文献
9.
孙秀真 《高等学校计算数学学报(英文版)》2000,(2)
I intreductiouInexact programs have been introduced by Soyster L4), and most of the results are givenby Soyster L6J-- LS], Falk [fi and Promerol L4J. The optimization problem described bySoyster is as follows:where the binds operation "+" refers the addition of sets. K, are non--empty convex sets,and K(b) ~ {ye r 1 y相似文献
10.
In this paper,a global optimization algorithm is proposed for nonlinear sum of ratios problem(P).The algorithm works by globally solving problem(P1) that is equivalent to problem(P),by utilizing linearization technique a linear relaxation programming of the (P1) is then obtained.The proposed algorithm is convergent to the global minimum of(P1) through the successive refinement of linear relaxation of the feasible region of objective function and solutions of a series of linear relaxation programming.Nume... 相似文献
11.
A descent algorithm for nonsmooth convex optimization 总被引:1,自引:0,他引:1
Masao Fukushima 《Mathematical Programming》1984,30(2):163-175
This paper presents a new descent algorithm for minimizing a convex function which is not necessarily differentiable. The
algorithm can be implemented and may be considered a modification of the ε-subgradient algorithm and Lemarechal's descent
algorithm. Also our algorithm is seen to be closely related to the proximal point algorithm applied to convex minimization
problems. A convergence theorem for the algorithm is established under the assumption that the objective function is bounded
from below. Limited computational experience with the algorithm is also reported. 相似文献
12.
针对恒模算法(CMA)收敛速度较慢、收敛后均方误差较大的缺点,提出一种新的双模式盲均衡算法.在算法初期,利用能快速收敛的归一化恒模算法(NCMA)进行冷启动,在算法收敛后切换到判决引导(DD-LMS)算法,减少误码率.计算机仿真表明,提出的新算法有较快的收敛速度和较低的误码率. 相似文献
13.
A rank-one algorithm is presented for unconstrained function minimization. The algorithm is a modified version of Davidon's variance algorithm and incorporates a limited line search. It is shown that the algorithm is a descent algorithm; for quadratic forms, it exhibits finite convergence, in certain cases. Numerical studies indicate that it is considerably superior to both the Davidon-Fletcher-Powell algorithm and the conjugate-gradient algorithm. 相似文献
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15.
提出了一种凸组合共轭梯度算法,并将其算法应用到ARIMA模型参数估计中.新算法由改进的谱共轭梯度算法与共轭梯度算法作凸组合构造而成,具有下述特性:1)具备共轭性条件;2)自动满足充分下降性.证明了在标准Wolfe线搜索下新算法具备完全收敛性,最后数值实验表明通过调节凸组合参数,新算法更加快速有效,通过具体实例证实了模型的显著拟合效果. 相似文献
16.
针对模糊C均值算法用于图像分割时对初始值敏感、容易陷入局部极值的问题,提出基于混合单纯形算法的模糊均值图像分割算法.算法利用Nelder-Mead单纯形算法计算量小、搜索速度快和粒子群算法自适应能力强、具有较好的全局搜索能力的特点,将混合单纯形算法的结果作为模糊C均值算法的输入,并将其用于图像分割.实验结果表明:基于混合单纯形算法的模糊均值图像分割算法在改善图像分割质量的同时,提高了算法的运行速度. 相似文献
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18.
负权最短路问题的新算法 总被引:3,自引:0,他引:3
Bellman-Ford算法自1958年以来一直是负权最短路问题的公认的最好算法之一.1970年,Yen对其进行了改进,理论上可以节省一半的计算量.本文得到了一种比Bellman-Ford算法更加优越的算法.尽管在理论上新算法无法保证完全超越于Yen的改进算法,但在许多情况下需要更少的计算量. 相似文献
19.
固定序算法是Bellman-Ford算法的一种基本改进算法。为了改变固定序算法在稀疏图上的劣势,本文通过预先订制参与迭代的点的计算顺序,对该算法进行了改进。实验表明,在稀疏图上, 改进后的算法相对于原算法计算效率提高了近50%, 并能够与国际流行的先进先出算法相媲美。本文的工作表明,固定序算法不仅在大规模稠密图上具有明显的优势,而且在稀疏图上也具有很强的竞争力。 相似文献
20.
Neculai Andrei 《Numerical Functional Analysis & Optimization》2019,40(13):1467-1488
A new diagonal quasi-Newton updating algorithm for unconstrained optimization is presented. The elements of the diagonal matrix approximating the Hessian are determined as scaled forward finite differences directional derivatives of the components of the gradient. Under mild classical assumptions, the convergence of the algorithm is proved to be linear. Numerical experiments with 80 unconstrained optimization test problems, of different structures and complexities, as well as five applications from MINPACK-2 collection, prove that the suggested algorithm is more efficient and more robust than the quasi-Newton diagonal algorithm retaining only the diagonal elements of the BFGS update, than the weak quasi-Newton diagonal algorithm, than the quasi-Cauchy diagonal algorithm, than the diagonal approximation of the Hessian by the least-change secant updating strategy and minimizing the trace of the matrix, than the Cauchy with Oren and Luenberger scaling algorithm in its complementary form (i.e. the Barzilai-Borwein algorithm), than the steepest descent algorithm, and than the classical BFGS algorithm. However, our algorithm is inferior to the limited memory BFGS algorithm (L-BFGS). 相似文献