例句與造句

1. On a class of quadratic programming problem with equality constraints
   一類帶有等式約束的二次規(guī)劃問題
2. Fixed iterative method for solving the inequality constrained quadratic programming problem
   不等式約束二次規(guī)劃的不動點迭代
3. An interior point algorithm for convex quadratic programming problem with box constraints
   框式約束凸二次規(guī)劃問題的內點算法
4. A global convergence inner - point style algorithm for generic quadratic programming problem
   二次規(guī)劃問題的一個全局收斂的內點型算法
5. Training svm can be formulated into a quadratic programming problem . for large learning tasks with many training examples , off - the - shelf optimization techniques quickly become intractable in their memory and time requirements . thus , many efficient techniques have been developed
   訓練svm的本質是解決一個二次規(guī)劃問題，在實際應用中，如果用于訓練的樣本數(shù)很大，標準的二次型優(yōu)化技術就很難應用。
6. It's difficult to find quadratic programming problem in a sentence. 用quadratic programming problem造句挺難的
7. 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-對偶內點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)劃問題的一個修正原始-對偶內點算法。
8. 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
   本文根據隋允康教授提出的二級控制理論，將二維連續(xù)體結構的形狀優(yōu)化問題處理成序列二次規(guī)劃問題進行了優(yōu)化。
9. 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)解之間的關系，進而構造了一個非線性l _ 1問題的下降算法，并證明了該算法的收斂性。
10. 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
    只要事先假設出所求函數(shù)的表達式，然后根據已知的微分關系和邊界條件對待求函數(shù)進行約束將原問題轉化為二次規(guī)劃問題，再采用支持向量機回歸算法對樣本進行學習即可求出參數(shù)，確定待定函數(shù)的關系式。
11. 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)劃這樣一個特殊的優(yōu)化問題，通過優(yōu)化，每個向量(樣本)被賦予一個權值，權值不為0的向量稱為支持向量，分類超平面是由支持向量構造的。
12. Based on the statistical learning theory and optimization theory , svms have been successfully applied to many fields such as pattern recognition , regression and etc . training an svm amounts to solving a convex quadratic programming problem . in this paper we do some researches on svms by the optimization theory and method
    它將機器學習問題轉化為求解最優(yōu)化問題，并應用最優(yōu)化理論構造算法來解決問題，本文主要是從最優(yōu)化理論和算法的角度對支持向量機中的最優(yōu)化問題進行研究。