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linear optimization中文是什么意思

  • 線性優(yōu)化
  • 線性最佳化
  • 線性最優(yōu)化

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  • 例句與用法
  • Design and implementation of linear optimization solving system
    大規(guī)模線性優(yōu)化求解系統(tǒng)的設(shè)計與實現(xiàn)
  • Based on theory of convex cones , our extensions of robust linear optimization are done in three directions
    摘要本文基于凸錐理論對魯棒線性最優(yōu)化作了若干拓展。
  • The video - based 3d body tracking method under the non - linear optimization framework was proposed , which combined multiple cues and motion prior efficiently
    其算法特點是,多種圖像特征和運動知識有機地集成于一個基于非線性優(yōu)化策略的跟蹤框架中。
  • Read sections 7 . 1 - 7 . 3 of chapter 7 . we will cover the basics of linear optimization , including formulations , key concepts , and graphical solution methods
    閱讀第七章7 . 1到7 . 3的部份。我們的課程將介紹線性最佳化的基礎(chǔ),包括方程式,重要概念以及圖形解方法。
  • A comprehensive set of lecture notes , from basic principles , such as linear optimization , to sophisticated real - world applications are available . also , there are problem sets in pdf format
    本課程提供詳盡的課堂講稿,涵蓋線性規(guī)劃等基本原理以及復(fù)雜的實際問題應(yīng)用。同時,本課還有一些問題集( pdf格式) 。
  • Especially , a kind of non - linear optimization analytic hierarchy process ( ahp ) with experts reliability on the basis of the traditional ahp is proposed , and it is a new method for determining the evaluation indexes weights
    特別地,文中在確定評價指標(biāo)的權(quán)重時,在傳統(tǒng)的層次分析法基礎(chǔ)上進行改進提出了一種新方法帶有專家可信度的非線性優(yōu)化層次分析法。
  • Traditional method can be classified two class : linear optimization technique and nonlinear optimization technique , linear optimization technique base on born approximation or rytov approximation is usually used to solve weak scattering problem
    線性優(yōu)化方法采用線性近似忽略了散射體內(nèi)部的多次散射,可以有效的反演低對比度的問題,但對于高對比度問題的求解則有可能不收斂。
  • By using rac ( radial alignment constraint ) of imaging process to decompose camera parameters and organizing the solving sequence of the parameters rationally , all parameters can be obtained through solving linear equations that avoid non - linear optimization
    巧妙地利用成像過程中的徑向約束( rac )分解攝像機參數(shù),使得求解線性方程組即可得到全部的攝像機參數(shù),避免非線性優(yōu)化搜索。
  • Compared with csm , two examples proved that ann could be trained successfully , even if the available data were insufficient and irregular , while csm showed the limit in selecting model type and non - linear optimization
    兩個實例的應(yīng)用結(jié)果表明:人工神經(jīng)網(wǎng)絡(luò)通過神經(jīng)原作用函數(shù)的簡單復(fù)合就能逼近有限子集的任意非線性函數(shù),而傳統(tǒng)的統(tǒng)計方法則存在著如何選擇模型形式及非線性優(yōu)化問題,表現(xiàn)出明顯的局限性,并且統(tǒng)計模型的更新工作相當(dāng)繁重。
  • With analogizing the evolution process of atomic transition from excited states to ground state , we proposed a novel non - linear optimization algorithm for geophysical inverse problem , called as simulated atomic transition algorithm ( sata )
    在此基礎(chǔ)上,模擬了物理學(xué)中原子從激發(fā)態(tài)向基態(tài)躍遷的物理過程,建立了一種與原子躍遷過程相對應(yīng)的非線性隨機躍遷數(shù)學(xué)模型和模型解躍遷搜索準則,導(dǎo)出了適用于一般地球物理資料的模擬原子躍遷的非線性反演算法。
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