熵不等式 的英文怎麼說

中文拼音 [shāngbùděngshì]
熵不等式 英文
entropy inequality
  • 熵 : 名詞[物理學] entropy; thermal charge
  • 不 : 名詞[書面語] （剁物所用的木墩） a block of wood
  • 等 : Ⅰ量詞1 （等級） class; grade; rank 2 （種; 類） kind; sort; type Ⅱ形容詞（程度或數量上相同） equa...
  • 式 : 名詞1 （樣式） type; style 2 （格式） pattern; form 3 （儀式; 典禮） ceremony; ritual 4 （自然科...
  • 等式 : [數學] equation; equality等式約束 equality constraint; 等式組 equality system
  1. Shannon entropy function is used to smoothing it. a non - interior predictor - corrector continuation method is built for a class of mixed complementarity problems

    基於非線性互補問題的一個等價不動點格式，第五章分別用shannon熵函數和kullback熵函數光滑化max函數。
  2. In the fields of fluid dynamics, entropy inequality reflects the second law of thermodynamics. i. e. entropy must increase across shock waves ( a kind of discontinuity ). all kind of approximate schemes should reflect the fact that it must satisfies some kind of discrete entropy inequality ). from the view of practical computation, stability and theo - retical error of any kind discrete schemes all dependend of the smoothness of the solution of ( 0. 2. 1 ). generally, the approximate solution have good stability and theoretial error in the area where the solutions have more regularity and poor stability and theoretial error in other area

    從流體力學來看，它事實上是熱力學第二定理的反映，即熵越過激波（一種間斷）要增加。各種估計格式構造的估計解應反映這一事實，即滿足熵不等式。從實際計算來看，總是通過離散化求解，不考慮計算的積累誤差，它的穩定性與計算精度都依賴與真解的光滑性，一般說，在解較光滑的區域有較好的穩定性與計算精度，而在較粗糙的區域則相反。
  3. The second chapter reveals the mathematical essence of entropy regularization method for the finite min - max problem, through exploring the relationship between entropy regularization method and exponential penalty function method. the third chapter extends maximum entropy method to a general inequality constrained optimization problem and establishes the lagrangian regularization approach. the fourth chapter presents a unified framework for constructing penalty functions by virtue of the lagrangian regularization approach, and illustrates it by some specific penalty and barrier function examples

    第一章為緒論，簡單描述了熵正則化方法與罰函數法的研究現狀；第二章，針對有限極大極小問題，通過研究熵正則化方法與指數（乘子）罰函數方法之間的關系，揭示熵正則方法的數學本質；第三章將極大熵方法推廣到一般不等式約束優化問題上，建立了拉格朗日正則化方法；第四章利用第三章建立的拉格朗日正則化方法，給出一種構造罰函數的統一框架，並通過具體的罰和障礙函數例子加以說明。
  4. In this thesis, we extend the entropy regularization method in two ways : from the min - max problem to general inequality constrained optimization problems and from the entropy function to more general functions

    本文從兩個方面發展了這種熵正則化方法，即將其從極大極小問題推廣到一般不等式約束優化問題上和用一般函數代替熵函數作正則項，建立新的正則化方法。
  5. An entropic smoothing method for solving convex inequality problems

    求解凸不等式問題的熵光滑化方法
  6. The pheromone - based parameterized probabilistic model for the aco algorithm is presented as the solution construction graph that the combinatorial optimization problem can be mapped on. based on the solution construction graph, the unified framework of the aco algorithm is presented. an iterative update procedure of the solutions distribution in the problem ' s probabilistic model is proposed, that will converge to the optimal solutions with probability one, then the minimum cross - entropy pheromone update rule is proposed to approximate the iterative update procedure by minimizing the cross - entropy distance and monte - carlo sampling

    基於解空間參數化概率分佈模型，首先提出了一個以概率1收斂于最優解的解空間概率分佈的迭代更新過程，然後提出了通過最小化不同分佈間的交互熵距離以及蒙特卡洛采樣來逼近此迭代過程的最小交互熵信息素更新規則，接著分別給出了弧模式以及結點模式信息素分佈模型下的最小交互熵等式。
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