superlinear convergence 中文意思是什麼

superlinear convergence 解釋
超線性收斂性
  • superlinear : 超線性
  • convergence : n. 1. 聚合，會聚，輻輳，匯合。2. 集合點；【數、物】收斂；【生物學】趨同（現象）。
  1. It exploits the structured of the hessian matrix of the objective function sufficiently. an attractive property of the structured bfgs method is its local superlinear / quadratic convergence property for the nonzero / zero residual problems. the local convergence of the structured bfgs method has been well established

    它們充分利用了目標函數的hesse矩陣的結構以提高演算法的效率，該演算法的顯著優點是對于零殘量問題具有二階收斂性而對于非零殘量問題具有超線性收斂性。
  2. Furthermore, the global and superlinear convergence of the shamanskii modification of the newton method with the new line search are proved under the weaker conditions than those in ref [ 10 ] ( i. e.,

    在本章中，我什1將邪a ？ nans汕6修正牛頓法的迭代形式作了進一步的改進，改進后的sha 。 a 。
  3. Using the conic function model local approximation, w. cdavidon ( 1980 ) proposed a class of iterative algorithms with modified matrix combining function value, furthermore under the theory d. c. sorensen has used local quadratic approximation method, then applying collinear scaling idea improving on the above algorithm and generalizing it, getting a class of collinear scaling algorithm, unifying former quasi - newton. in the paper, using local quadratic approximation method, the first, constructing the new collinear scaling gene, getting a class of the new collinear scaling algorithm with briefness and numerical stability, ., we discusses some properties of the algorithm and its local linear convergence, q - superlinear convergence and the whole convergence ; secondly we have made numerical experimentation and numerical analysis ; the last, we have done much discussion for collinear scaling idea and given the several new collinear scaling algorithm

    本文的工作就是基於局部二次逼近原理，首先通過構造新的共線調比因子，得到了一類新的更簡潔，數值穩定性更好的共線調比演算法，進而我們給出了本共線調比演算法的局部收斂性，全局收斂性以及演算法q -超線性速度的理論證明；其次，用經典的無約束優化五大考核函數就本共線調比演算法進行了數值試驗和數值分析；最後，就局部二次逼近思想，進行共線調比演算法思想進行更廣泛的討論，給出了幾個新共線調比演算法。
  4. Using the comparison principle, it is proved that the proposed method is of superlinear convergence

    利用比較原理，間接證明該演算法是一種具有超線性收斂性的近似牛頓法。
  5. Under appropriate conditions, we obtain the global and superlinear / qudratic convergence of the method. the reported numerical results show that the proposed method performs well for the test problems

    數值計算結果表明，我們的演算法對普通非線性方程組有良好效果，但優勢在求解病態方程組時表現得更為明顯，是解非線性方程組一種行之有效的方法。
  6. Under mild conditions, we prove the global and superlinear convergence of the method

    在較弱的條件下，得到了演算法的全局收斂性及其超線性收斂性。
  7. A feasible sqp algorithm with superlinear convergence for inequality constrained optimization

    不等式約束優化一個具有超線性收斂的可行序列二次規劃演算法
  8. Lc1 unconstrained optimization problem was discussed in the second chapter, giving a new trust region method and proving its global convergence and superlinear convergence under some mild conditions

    給出了一個新的信賴域演算法，並在一定的條件下證明了演算法的全局收斂性和局部超線性收斂性。
  9. We then develop a bfgs method for solving the nonsmooth equation. the method possess some descent property. under mild conditions, we establish the global and superlinear convergence of the proposed method

    在此基礎上，我們提出一種單調下降的線性搜索，進而提出求解該非光滑方程組的具有單調下降性的bfgs演算法。
  10. The general shamanskii modification of the newton method is defined by the iteration and the global and superlinear convergence of the general shamanskii modification of the newton method are proved in this dissertation

    我們不僅證明了改進的sha一mansk燈修正牛頓法的全局收斂性，而且也證明該方法具有超線性收斂速度
  11. In chapter 2. we give a class of new algorithms for nonlinear programming problems with linear constrained by combining the gradient projection method with non - quasi - newton method which was given in paper [ 2 ]. it ' s global convergence and the superlinear convergence are proved under suitable conditions

    在第二章中我們將梯度投影與文[ 2 ]中的非擬牛頓法相結合，給出了求解線性約束非線性優化問題的一類梯度投影非擬牛頓演算法。
  12. In chapter 3, we give a class of new algorithms with inexact search for nonlinear programming problems with linear constrained by combining the generalized projection method with non - quasi - newton method. it ' s global convergence and the superlinear convergence are proved under suitable conditions

    新演算法推廣了文[ 1 ， 2 ]中的結果。在第三章中我們將廣義投影演算法與非擬牛頓法相結合，給出了求解線性約束非線性優化問題的一類廣義投影非擬牛頓演算法。
  13. In the third chapter we discuss lc1 constrained optimization problem. to solve it, we turn it into nonsmooth equations, utilizing inexact theory we give an inexact generalized newton ' s method and under some mild conditions we prove that it is global convergence and superlinear convergence

    首先將其約束問題的求解轉化為非光滑方程組的求解，然後利用不完全求解理論給出了一個非精確的廣義牛頓演算法，在一定的條件下證明了演算法的全局收斂性和局部超線性收斂性並給出了lc ~ 1非線性約束問題的收斂性條件。
  14. Consequently, the convergence rate of the proposed method is superlinear. to speed up the method, we combine the structured mbfgs method with gauss - newton method to propose a hybrid method

    為了加快演算法的收斂速度，我們結合guass - newton法和結構化mbfgs法提出一種雜交方法，證明了這個雜交方法的全局收斂性。
  15. Because three systems of equations solved at each iteration have the same coefficients, so the ammount of computation are less than that of the existing sqp algorithms. under some common conditions ( such as the second order sufficient condition ) which are used in some references, we prove that the algorithm possesses not only global convergence, but also strong convergence and superlinear convergence

    該演算法在每次迭代時所需解的三個線性方程組具有相同的系數，因此計算量要比現有的sqp方法有所減少；在與一些文獻平行的假設條件（如二階充分條件）下，論文證明了演算法不僅具有全局收斂性，而且還具有強收斂性和超線性收斂性
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