非线性最小二乘参数平差的非线性规划算法研究
Nonlinear Programming Algorithms for Nonlinear Least Squares Adjustment by Parameters
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摘要: 讨论了非线性最小二乘参数平差可行的5种非线性规划算法———牛顿法、最速下降法、离散牛顿法、拟 牛顿法和SQPM算法,通过分析、比较和实算证实SQPM算法是求解非线性最小二乘参数平差问题的最为有力 的工具,且使SQPM算法成为无需精确计算参数概略值的非线性最小二乘参数平差法。Abstract: Five feasible nonlinear programming algorithms dealing with nonlinear least squares adjustment by parameters are discussed. They are Newton method, speediest descending method, discrete Newton method, quasi-Newton method, and sequential quadratic programming method (SQPM). It is confirmed by analysis, comparison, and computation examples that SQPM is the most powerful tool to solve the problem of nonlinear least squares adjustment by parameters, without exactly computing the approximation of parameters.
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Key words:
- least square methods /
- nonlinear programming /
- algorithms /
- adjustment by parameter
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