unconstrained optimization

"unconstrained optimization"的翻譯和解釋

"查查詞典"手機(jī)版

千萬(wàn)人都在用的超大詞匯詞典翻譯APP

例句與用法

Meanwhile , we point out the privilege and defective of cim . the fourth chapter is the main body of this paper . we explore how to apply the cim to solve unconstrained optimization problems effectively
第四章是本文的主要部分，探討了錐模型信賴域子問(wèn)題的求解及不完全錐函數(shù)插值模型算法的數(shù)值實(shí)現(xiàn)。
In the first chapter , we simply review origin , development and characteristics of nonlnear conjugate gradient method for solving unconstrained optimization , introduce some important formulas of the method
在第一章中，我們簡(jiǎn)要回顧了求解無(wú)約束優(yōu)化的非線性共軛梯度法的產(chǎn)生、發(fā)展和特點(diǎn)，介紹了這種方法的一些重要形式。
In unconstrained optimization , we deal with the standard problem of finding the minimum of a function f : rn - r . if we assume that the function is twice continuously differentiable , many methods can be applied to find the minimum
在無(wú)約束最優(yōu)化中，考慮尋找f ： r ~ n r的極小點(diǎn)。假設(shè)f是二次連續(xù)可微的，可以有許多方法尋找它的極小點(diǎn)。
An algorithm of solving nonlinear coupled equations is given which transforms the solving problem to unconstrained optimization problem and the coupled equations are solved by a genetic algorithm and newton iteration
同時(shí)還給出了一種求解非線性方程組的算法，即將非線性方程組的求解問(wèn)題轉(zhuǎn)化為帶約束的優(yōu)化問(wèn)題，應(yīng)用遺傳算法和牛頓迭代法求解。
The topics covered in this course include : unconstrained optimization methods , constrained optimization methods , convex analysis , lagrangian relaxation , nondifferentiable optimization , and applications in integer programming
這門課程的主題包括：無(wú)限制最適化方法，限制最適化方法，凸分析，拉格朗日松弛法，不可微分函數(shù)最適化，以及在整數(shù)規(guī)劃上的應(yīng)用。
Especially parameter estimation algorithms are discussed and a new method based on bfgs and differential precise linear searching is proposed to solve the unconstrained optimization problem successfully deriving from parameter estimation
研究了機(jī)理模型參數(shù)估計(jì)算法，提出了基于bfgs變尺度和微分精確線性搜索的優(yōu)化算法求解參數(shù)估計(jì)優(yōu)化問(wèn)題，取得了理想的收斂效果。
For the quasi - newton type trust region method based on the conic model solving unconstrained optimization , horizon vector of the conic medel is proposed , the unique optimal parameter is determined and the numerical results are given in this paper
摘要對(duì)于求解無(wú)約束優(yōu)化問(wèn)題的錐模型擬牛頓型信賴域方法，本文主要討論了水平向量的選取及最優(yōu)參數(shù)的確定，并給出了數(shù)值試驗(yàn)結(jié)果。
Newton - pcg algorithm is very efficient for solving the unconstrained optimization problems . we considered an integer one - dimensional optimization problem appeared in newton - pcg method . the efficiency of newton - pcg algorithm is depended on the optimal value of this problem
Newton - pcg算法是解決無(wú)約束問(wèn)題的有效方法，在該算法中需要求解一個(gè)一維整數(shù)最優(yōu)化問(wèn)題，并且算法的效率也依賴于它的最優(yōu)值。
Zhu has studied unconstrained optimal problem by combining optimal path and modified path with nonmonotonic trust region methods in [ 9 ] , and the use of l2 norm by trust region method in [ 1 ] in seeking the solution of unconstrained optimization has formed simple approximate trust region path , which enlightens us to solve nonlinear programming problem by curvilinear path
朱德通在文[ 9 ]中將最優(yōu)路徑和修正梯度路徑與非單調(diào)信賴域方法相結(jié)合討論無(wú)約束優(yōu)化問(wèn)題，且文[ 3 ]中用信賴域方法解無(wú)約束優(yōu)化問(wèn)題取l _ 2范數(shù)形成了形式簡(jiǎn)單的近似信賴域路徑，此類思想啟發(fā)作者用弧線路徑來(lái)解決約束優(yōu)化問(wèn)題。
This paper mainly concerns the conic interpolation model method for unconstrained optimization and its implementation , the structure of which is organized as follows : firstly , we survey the history of the direct search methods concisely , and summarize the methods that are currently considered to be effective for unconstrained optimization
首先介紹了直接搜索方法的發(fā)展概況，歸納總結(jié)了目前比較有效的各種算法。其次論文對(duì)于數(shù)值試驗(yàn)結(jié)果較好的二次模型插值算法和發(fā)展概況做了概要性的介紹。