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quadratic programming problem中文什么意思

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  1. In order to improve the efficiency of the algorithm , we not only correct some defects of the primal - dual interior point algorithm in [ 4 ] , but also give a modified primal - dual interior point algorithm for convex quadratic programming problem with box constraints
    為提高算法的有效性，對文[ 4 ]所給的原始-對偶內(nèi)點(diǎn)算法理論上的某些缺陷加以更正，并給出框式約束凸二次規(guī)劃問題的一個(gè)修正原始-對偶內(nèi)點(diǎn)算法。
  2. A program of shape optimization for two - dimensional continuum structures , which is carried out on msc . patran & nastran panel , is described in this thesis , according to a two - phase control theory presented by professor sui yunkang , the optimization is treated as a sequential quadratic programming problem
    本文根據(jù)隋允康教授提出的二級控制理論，將二維連續(xù)體結(jié)構(gòu)的形狀優(yōu)化問題處理成序列二次規(guī)劃問題進(jìn)行了優(yōu)化。
  3. For nonlinear l1 problem based on the conditions for optimality of the nonlinear l1 problem in [ 1 ] , we first discuss the descent direction of the objective function f ( x ) in theory , further more , we study the relation between the optimal solution of nonlinear l1 problem and the optimal solution of some kind of quadratic programming problem with box constrains . hence , we construct a descent algorithm for nonlinear l1 problem and prove the convergence of the algorithm
    在文[ 1 ]所給的最優(yōu)性條件的基礎(chǔ)上，對非線性l _ 1問題從理論上研究了f ( x )的下降方向、最優(yōu)解與某種框式約束最小二乘問題的最優(yōu)解之間的關(guān)系，進(jìn)而構(gòu)造了一個(gè)非線性l _ 1問題的下降算法，并證明了該算法的收斂性。
  4. When solving the problems , we use the support vector regression ( svr ) . first assuming the formula of function , then according to the differential and boundary conditions we transform the original problem to the quadratic programming problem . finally , use the learning algorithm of svr to decide the parameters
    只要事先假設(shè)出所求函數(shù)的表達(dá)式，然后根據(jù)已知的微分關(guān)系和邊界條件對待求函數(shù)進(jìn)行約束將原問題轉(zhuǎn)化為二次規(guī)劃問題，再采用支持向量機(jī)回歸算法對樣本進(jìn)行學(xué)習(xí)即可求出參數(shù)，確定待定函數(shù)的關(guān)系式。
  5. The separating plane with maximal margin is the optimal separating hyperplane which has good generation ability . to find a optimal separating hyperplane leads to a quadratic programming problem which is a special optimization problem . after optimization all vectors are evaluated a weight . the vector whose weight is not zero is called support vector
    而尋找最優(yōu)分類超平面需要解決二次規(guī)劃這樣一個(gè)特殊的優(yōu)化問題，通過優(yōu)化，每個(gè)向量(樣本)被賦予一個(gè)權(quán)值，權(quán)值不為0的向量稱為支持向量，分類超平面是由支持向量構(gòu)造的。

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