STOCHASTIC EXTREMUM SEEKING IN THE PRESENCE OF CONSTRAINTS

STOCHASTIC EXTREMUM SEEKING IN THE PRESENCE OF CONSTRAINTS
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存在约束条件下的随机极值搜索

DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
S. Alves
S. Alves
中科院分区：
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文献类型：
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作者：
F. Coito;J. M. Lemos;S. Alves

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摘要在随机框架下，研究了自变量约束下全局未知函数的自适应最小化问题。本文的主要贡献在于将CAM算法推广到向量问题。通过对随机算法和奇异摄动方法的ODE分析，证明了向量情况下唯一可能的收敛点是约束的局部极小值。对二维问题的仿真说明了这一结果。
Abstract The problem of adaptive minimization of globally unknown functions under constraints on the independent variable is considered in a stochastic framework. The main contribution of this paper consists in the extension of the CAM algorithm to vector problems. By resorting to the ODE analysis for analyzing stochastic algorithms and singular perturbation methods, it is shown that the only possible convergence points in the vector case are the constrained local minima. Simulations for dimension 2 problems illustrate this result.