subproblem 中文意思是什麼

subproblem 解釋
部分問題
  1. However, in existing global newton ' s methods a linearized variational inequality subproblem has to be solved at each iteration, whose computational cost is equivalent with a qp problem, and the local fast convergence is usually established theoretically incompletely

    通過應用fiseher一burmeister非線性互補問題函數， h . qi和l . qi在17 ]中對以前的qp一free演算法做了有效的改進，使得迭代矩陣的一致非奇異性得到保證。
  2. On solution of quadratic subproblem

    關於二次子問題的解
  3. Furthermore, the authors develop the proposed alternating direction method as an inexact method, which only needs to solve the subproblem inexactly

    進一步，又提出了一類非精確交替方向法，每步迭代計算只需非精確求解子問題。
  4. In chapter 3, we give an equivalent form of semi - infinite programming, and a locally convergent ssle method is proposed for sip. we only need solve a linear system equations and a subproblem with a parameter per step, also a modified algorithm which saves cost of computations is given, at the end of the paper, we give a proof of the convergence for the algorithms

    第三章通過適當的變形，得到半無限規劃問題的一個等價形式，並給出一個局部收斂的序列線性方程組演算法，這個演算法在每一步，只需求解一個線性方程組和一個帶參數的非線性子問題，證明了演算法的收斂性，同時，給出了一個修正演算法，與前面演算法相比較，修正演算法節約一定的計算量，同樣具有較好的收斂性。
  5. A branch and bound algorithm for solving a class of nonlinear 0 - 1 knapsack problems is proposed, in which branching is common 0 - 1 variables one and a better feasible solution is found by a simply integer heuristic method as well as a lower bound of the optimal value of the subproblem in the each branching node is determined by solving linear programming relaxed approximate problem to be obtained with linear relaxed technique

    摘要構造出了一類可分離非線性0 - 1背包問題的分枝定界演算法，分枝的過程是普通的0 - 1變量分枝，用簡單的取整啟發式法確定更好的可行解；而在每個分枝結點處用線性鬆弛技術確定了它的子問題的一個線性規劃鬆弛逼近，由此得到最優值的一個下界。
  6. The aim of this paper is to construct a three - term conjugate gradient method to solve the trust region subproblem

    在本文中，我們提出了解信賴域子問題的三項預處理共軛梯度法，並將這個方法嵌入解大型最優化問題的信賴域演算法中。
  7. At first, on the basis of the sufficient and necessary optimality conditions, we give a certain algorithm to compute the trust region subproblem ; then, we draw out a different scheme for parameter vector in cim

    在分析子問題最優性條件的基礎上，我們給出了錐函數模型信賴域子問題的求解演算法；並從數據擬合的角度提出了對錐模型中參數向量的另外一種選擇方案。
  8. It is posssible that the trust region subproblem needs to be resolved many times before obtaining an acceptable step for the traditional trust region method, and hence the total computation for completing one iteration might be expensive

    對于傳統的信賴域方法，要獲得可接受的迭代步可能要通過重復計算多次信賴域子問題才能獲得，因此每完成一次迭代的整個計算會比較大。
  9. Based on double dogleg path, the iterative direction is always obtained in the intersection of double dogleg path and bound of trust region by solving the affine scaling trust region subproblem

    一般地，基於雙折線路徑方法可以在雙折線路徑和信賴域邊界相交點得到迭代步。
  10. The subproblem is solved by simulated annealing algorithm

    該子問題可通過模擬退火演算法來解決。
  11. The convergence theorem of the proposed method is proved based on the exact solution of the subproblem

    基於子問題的精確求解，該文證明了演算法的收斂性。
  12. When the hessian is positive definite, the qp subproblem is a strict convex quadratic programming

    若qp子問題的hessian陣正定，則它是一個嚴格凸二次規劃問題。
  13. The process will often be self - repeating since each subproblem may still be complex enough to require further decomposition

    由於每個子問題可能仍然十分復雜，需要進一步的分解，這個過程就將不斷的循環往復
  14. The method incorporates a primal partitioning scheme - with a network flow subproblem - to obtain good feasible solutions

    本文設計了一種模擬退火演算法的實現形式，通過大量的算例分析表明，該演算法具有良好的尋優特性與運算效率。
  15. In this new dividing strategy, the sum of the subproblem ' s scales is equal to the original problem ' s scale minus 1. an eigenvalue interlacing theorem is given and proved

    在這個劃分策略中，子問題的規模之和等於原問題的規模減1 ，文中給出了特徵值分割定理及其證明。
  16. A dynamic approach for the minimization subproblem in alm method is discussed, and then a neural network iterative algorithm is proposed for general constrained nonlinear optimization. 3

    使用增廣lagrange乘子法求解時，雖然可以避免罰參數無限增大的弊病，但同時也提出了一個難以求解的子命題。
  17. Otherwise, the trust region radius is reduced and a new trial point is selected. it is possible that the region subproblem need to be resolved many times before obtaining an acceptable step, and hence the total computation for completing one iteration might be expensive. this article combines approximate trust region path and nonmonotonic backtracking strategy to solve nonlinear optimization subject to linear inequality constraints, that is, we use approximate trust region path to get the research direction by minimuming quadratical model in the trust region by employing

    本文在求解線性不等式約束優化問題時，將近似信賴域路徑與非單調信賴域方法相結合，即在信賴域半徑內沿近似信賴域路徑得到一極小化二次模型的搜索方向後採用回代法避免重復求解信賴域子問題，在此演算法中當搜索方向不被接受時，就用非單調線搜索技術得到接受步長，定義新的迭代點。
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