convex programming 中文意思是什麼
convex programming
解釋
凸規劃- convex : adj. 中凸的，凸圓的，凸面的。n. 凸狀，凸面，凸圓體。 convex glasses 遠視眼鏡，老花眼鏡。adv. -ly
- programming : 編程序的
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The mathematical programming methods, both the method of moving asymptotes ( mma ) belonging to convex programming methods and the sequential linear programming method ( slp ), were used to solving optimization problems
用移動漸進線方法（ mma ）求解單目標優化問題，用序列線性規劃方法（ slp ）求解模糊目標混合規劃問題。 -
It is well known that wolfe type and mond - weir type duals are important duals in mathematical programming. professor xu ( ref. 35 ) firstly introduced a mixed type dual in multiobjective programming, which is more general and flexible than the above two duals. recently, it was pointed out that if a function is invex, then it is f - convex by ref. 37 and vice versa
我們知道，在所有的對偶規劃中， wolf型和mond - weir型對偶是兩類重要的對偶，而徐教授在文獻[ 35 ]中，又針對多目標規劃首次提出了混合型對偶的概念，它比以上提及的兩類重要對偶，更具一般性和靈活性。 -
Interior point approach for convex quadratic programming
凸二次規劃問題的內點演算法 -
Following the introductory first chapter and the second chapter on linear algebra and convex analysis, the book is organized into two parts : linear programming and networks flows
在第一章簡介和第二章線性代數和凸分析之後，本書分為兩部分：線性規劃和網路流。 -
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：無限制最適化方法，限制最適化方法，凸分析，拉格朗日鬆弛法，不可微分函數最適化，以及在整數規劃上的應用。 -
Theory of portfolio optimization is an important part of the modern ? nance in - vestment theories, which uses mathematical facilities such as convex analysis, random analysis, nonsmooth analysis, ( nonlinear ) programming etc, combined with the mean - variance method the basic method of modern portfolio theory. by setting up mathe - matical models, discussed the investment rules of ? nance market and o ? ered theoretic guide for investors
投資組合優化理論是現代金融投資理論的重要組成部分，它運用凸分析、隨機分析、非光滑優化、 （非）線性規劃等數學工具，並與現代投資組合理論的基本方法均值方差方法相結合，通過建立數學模型討論金融市場投資規律並為個人或機構投資者提供理論指導。 -
Abstract firstly, convert a programming problem with multiple constraints into a programming problem with single constraint, secondly, several convexification and concavification transformations for the constrained problem with strictly monotone constraint functions are proposed according to the transformed problem with single constraint, then this constrained programming problem can be converted into a concave minimization or a reverse convex programming problem
摘要首先將一個具有多個約束的規劃問題轉化為一個只有一個約束的規劃問題，然後通過利用這個單約束的規劃問題，對原來的多約束規劃問題提出了一些凸化、凹化的方法，這樣這些多約束的規劃問題可以被轉化為一些凹規劃、反凸規劃同題。 -
Topics covered include : randomized computation ; data structures ( hash tables, skip lists ) ; graph algorithms ( minimum spanning trees, shortest paths, minimum cuts ) ; geometric algorithms ( convex hulls, linear programming in fixed or arbitrary dimension ) ; approximate counting ; parallel algorithms ; online algorithms ; derandomization techniques ; and tools for probabilistic analysis of algorithms
主題包括?隨機計算、資料結構（雜湊表、省略串列） 、圖論演演算法（最小擴張樹，最短路徑，最少切割） 、幾何演演算法（凸殼、在固定或任意維度的線性規劃） 、近似計數、平行演演算法、線上演演算法、消去隨機技術，以及演演算法的機率分析工具。 -
Duality theorems of multiobjective programming for - arcwise convex function
弧式凸函數多目標規劃的對偶定理 -
This paper consists of three parts as follows, 1. a nonsmooth convex programming is relaxed to a smooth convex programming by using a cutting - plane, which is constructed by subgradient. an algorithm based on the cutting - plane is presented. in this way, a cutting plane algorithm and it ' s convergence for semide ? nite programming are provided
利用次微分的概念給出了一種非光滑凸規劃割平面的構造技巧，找到了半定規劃可行域的一個支撐超平面，從而給出了求解半定規劃的一種割平面演算法 -
Finally, the relationships between generalized set - valued variational inclusion problems and non - convex programming are studied
最後研究了廣義集值變分包含問題與非凸規劃之間的關系。 -
Finally, prove that any global optimal solution of the converted concave minimization problem or reverse convex programming problem obtained by the existing algorithms is an approximate global optimal solution of the original problem
最後，還證明了得到的凹規劃和反凸規劃的全局最優解就是原問題的近似全局最優解。 -
The convergence of an algorithm for the reverse convex programming
一類反凸規劃演算法的收斂性 -
The affine scaling algorithm for convex programming with linear constraints
線性約束凸規劃問題的仿尺度演算法 -
Convex programming problem
凸規劃問題 -
Solving linearly constrained convex programming by potential reduction interior - point algorithm
求解線性約束凸規劃的勢下降內點演算法 -
Fifthly, the parameter control methods for solving convex programming, especially linear programming and convex quadratic programming, are discussed and its convergence iv is probed
五是研究了凸規劃包括線性規劃和凸二次規劃的參數控制演算法及其收斂性。 -
Abstract : this paper presents a new primal ? dual interior point algorithm for a convex programming with box constraints, and prove the iteration complexity is polynomial
文摘:本文為框式約束的一類凸規劃提出了一個新的內點演算法，原始-對偶路徑跟蹤法，並證明了演算法的迭代復雜性為多項式時間性 -
Abstract : an algorithm of indefinite quadratic programming over unbound domain is presented ; the indefi nite quadratic programming is translated into a series of convex programming and the convergence of algorithm is discussed
文摘:給出了無界域上不定二次規劃的一個演算法，該演算法將不定二次規劃轉化為一系列凸二次規劃，並證明了演算法的收斂性 -
At the beginning of their research, they could transform some nonlinear programming into relevant dual, such transformation made some outcome almost similar to linear programming. but if they wanted meaningful results, they had to consider convex programming
在初期，他們發現可以將某些非線性規劃表述為一個相應的對偶規劃，且有少量相似於線性規劃的結果，但如果希望得到有意義的結果，只能考慮凸規劃。
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