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iterates相关的网络例句

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与 iterates 相关的网络例句 [注:此内容来源于网络,仅供参考]

The numerical ex-periment has shown that the number of iterations depends significantly on the positiveparameterα,β. But, in general, it is di?cult to choose a proper parameterα,β. Thuswe propose new self-adaptive alternating direction method which adjusts the scale pa-rameter automatically per iteration based on the message of the iterates. We have provedthe convergence of the method under mild assumptions.Finally, we present some numerical results.

对于我们提出的新交替方向法,数值实验表明,迭代的步数与时间与所取的参数α,β有关,然而我们很难去选择一个合适的参数α,β,因此我们提出新自适应交替方向法,对其中的参数α,β进行自适应调整,这种自适应算法根据每步迭代信息自动地调整参数α,β,并证明了算法的收敛性。

Rather than always iterating over an arithmetic progression of numbers, or giving the user the ability to dene both the iteration step and halting condition, Python's for statement iterates over the items of any sequence, in the order that they appear in the sequence.

通常的循环可能会依据一个等差数值步进过程或由用户来定义迭代步骤和中止条件,Python 的for 语句依据任意序列中的子项,按它们在序列中的顺序来进行迭代。

Rather than always iterating over an arithmetic progression of numbers, or giving the user the ability to define both the iteration step and halting condition, Python's for statement iterates over the items of any sequence, in the order that they appear in the sequence.

通常的循环可能会依据一个等差数值步进过程或由用户来定义迭代步骤和中止条件,Python 的 for 语句依据任意序列中的子项,按它们在序列中的顺序来进行迭代。

Rather than alwaysiterating over an arithmetic progression of numbers,or giving the user the ability to define both the iteration step andhalting condition, pythonsfor statement iterates over the items of anysequence, in the order that they appear inthe sequence.

通常的循环可能会依据一个等差数值步进过程或由用户来定义迭代步骤和中止条件,python 的 for 语句依据任意序列中的子项,按它们在序列中的顺序来进行迭代。

Furthermore, the first-order optimality condition and its equivalent reformulations for generalized semi-infinite max-min programming with a non-compact set are presented using the lower-Hadamard directional derivative and subdifferential.2. Chapter 3 studies the gradient-type methods for unconstrained optimization problems. Section 1 proposes a new class of three-term memory gradient methods. The global convergence property of the method is established. Furthermore, in order to improve the convergence property of the method, a new class of memory gradient projection methods is presented with the property that the whole sequence of iterates converges to a solution to the problem under the conditions such as pseudo-convexity and continuous differentiability of objective function. In section 2, two new classes of methods, called gradient-type method with perturbations and hybrid projection method with perturbations, are proposed. In these methods, non-monotone line search technique is employed, which makes them easily executed in computer.

第3章研究了无约束优化问题的梯度型算法,第1节提出了一类新的三项记忆梯度算法,讨论了算法的全局收敛性,进一步提出了一类新的具有更好收敛性质的记忆梯度投影算法,并证明了该算法在函数伪凸的情况下具有整体收敛性,第2节在非单调步长搜索下提出了带扰动项的梯度型算法及其混合投影算法,这两类算法的一个重要特征就是步长采用线搜索确定而不象许多文献中那样要求步长趋于零,这样更容易在计算机上实现,在较弱的条件下证明了这些算法的全局收敛性,数值算例表明了算法的有效性。

In this case, the for clause iterates each variable over its input sequence.

在这种情况下,for子句迭代每个变量到其输入序列。

Nevertheless, it is established that the sequence of iterates produced by the method of Hooke and Jeeves converges to the unique minimizer of f

然而,确定的是胡克和吉夫斯方法的叠代序列收敛于f的唯一最小值。

This method uses the strong non-linear mapping ability which the RBF network has and its short-time training merit to construct the network, substitutes the non-analytic function, make it to instate of the optimized question of the objective function, and iterates this objective function with the genetic algorithm to search the optimal solution.

该方法利用RBF网络所具有的非线性映射能力强,训练时间短的优点,构建网络,替代非解析函数,使之作为优化问题的目标函数,并用遗传算法搜索迭代出该目标函数的最优解。

In the traditional nonmonotone trust region method, the reference function value of the iteration point is the maximum function value of a prefixed number of previous iterates.

传统非单调信赖域方法中,迭代点的参考函数值取做前固定个点函数值中的最大值。

A self-adjusting mechanism is added to the new perturbed system such that the Newton directions of each complementary pairs can be adjusted self-adaptively according to the information of last iterates.

这样,在摄动方程本身建立了一种自调节机制,从而使牛顿方向能够根据上次迭代点的信息在各个互补对之间做出自适应的调整。

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