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用于线性优化的基于核函数的动态步长原-对偶内点算法 总被引：1，自引：2，他引：1
In this paper, primal-dual interior-point algorithm with dynamic step size is implemented for linear programming （LP） problems. The algorithms are based on a few kernel functions, including both serf-regular functions and non-serf-regular ones. The dynamic step size is compared with fixed step size for the algorithms in inner iteration of Newton step. Numerical tests show that the algorithms with dynaraic step size are more efficient than those with fixed step size. 相似文献
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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. 相似文献
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线性约束凸规划的一个新原-对偶路径-跟踪内点算法 总被引：1，自引：0，他引：1
In this paper, a primal-dual path-following interior-point algorithm for linearly constrained convex optimization （LCCO） is presented. The algorithm is based on a new technique for finding a class of search directions and the strategy of the central path. At each iteration, only full-Newton steps are used. Finally, the favorable polynomial complexity bound for the algorithm with the small-update method is deserved, namely, O（√nlog n/ε）. 相似文献
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1 Introduction Interior-point methods (IPMs) for semidefinite opti-mization (SDO) have been studied intensively,due totheir polynomial complexity and practical efficiency.In the past decade , SDO has become a popular re-search area in mathematical programming when it be-came clear that the algorithm for linear opti mization(LO) can often be extended to the more general SDOcase. Other two factors are also responsible for thisincreasing interest in SDO. Firstly, SDO has a wideapplication… 相似文献
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The choice of self-concordant functions is the key to efficient algorithms for linear and quadratic convex optimizations,
which provide a method with polynomial-time iterations to solve linear and quadratic convex optimization problems. The parameters
of a self-concordant barrier function can be used to compute the complexity bound of the proposed algorithm. In this paper,
it is proved that the finite barrier function is a local self-concordant barrier function. By deriving the local values of
parameters of this barrier function, the desired complexity bound of an interior-point algorithm based on this local self-concordant
function for linear optimization problem is obtained. The bound matches the best known bound for small-update methods.
Project supported by the National Natural Science Foundation of China (Grant No.10771133), the Shanghai Leading Academic Discipline
Project (Grant No.S30101), and the Research Foundation for the Doctoral Program of Higher Education (Grant No.200802800010) 相似文献
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Suboptimal alignments always reveal additional interesting biological features and have been successfully used to informally estimate the significance of an optimal alignment. Besides, traditional dynamic programming algorithms for sequence comparison require quadratic space, and hence are infeasible for long protein or DNA sequences. In this paper, a space-efficient sampling algorithm for computing suboptimal alignments is described. The algorithm uses a general gap model, where the cost associated with gaps is given by an affine score, and randomly selects an alignment according to the distribution of weights of all potential alignments. If x and y are two sequences with lengths n and m, respectively, then the space requirement of this algorithm is linear to the sum of n and m. Finally, an example illustrates the utility of the algorithm. 相似文献
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In this paper, a new primal-dual interior-point algorithm for convex quadratic optimization (CQO) based on a kernel function is presented. The proposed function has some properties that are easy for checking. These properties enable us to improve the polynomial complexity bound of a large-update interior-point method (IPM) to O (√nlognlogn/ε), which is the currently best known polynomial complexity bound for the algorithm with the large-update method. Numerical tests were conducted to investigate the behavior of the algorithm with different parameters p, q and θ, where p is the growth degree parameter, q is the barrier degree of the kernel function and θ is the barrier update parameter. 相似文献
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A reduction of truss topology design problem formulated by semidefinite optimization (SDO) is considered. The finite groups and their representations are introduced to reduce the stiffness and mass matrices of truss in size. Numerical results are given for both the original problem and the reduced problem to make a comparison. 相似文献
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A polynomial interior-point algorithm is presented for monotone linear complementarity problem (MLCP) based on:a class of kernel functions with the general barrier term, which are called general kernel functions. Under the mild conditions for the barrier term, the complexity bound of algorithm in terms of such kernel function and its derivatives is obtained. The approach is actually an extension of the existing work which only used the specific kernel functions for the MLCP. 相似文献
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