Arsham H. Gradient-based optimization techniques for dis- crete event systems simulation. In Wah BW (ed.), Wiley Encyclopedia of Computer Science and Engineering. New York: John Wiley & Sons, 2009, pp. 110-127.systems simulation. Simulation Practice and Theory, 6(1),...
Then, based on the optimization strategy chosen, these functions are optimized simultaneously over the region of interest to determine the systems optimum operating conditions. For nominal-the best type (N-Type)-also known as target is the best- quality characteristics, Vining and Myers [3] ...
aExamples of non-gradient-based optimization techniques are adaptive simulated annealing, Hooke-Jeeves direct search, and genetic algorithm (GA). 基于非梯度的优化技术的例子是能适应的被模仿的焖火、Hooke-Jeeves直接查寻和基因算法 (GA)。[translate]...
OurFlexiCubesrepresentation is flexible enough that objectives and regularizers which depend on the extracted mesh itself can be directly evaluated with automatic differentiation and incorporated into gradient-based optimization. Here we apply adevelopabilityterm on extracted mesh to encourages fabricability f...
aHybrid optimization techniques use the combination of both non-gradient based and gradient-based techniques subsequently in order to take the advantages and reduce the disadvantages of single optimization technique. 杂种优化技术使用非梯度随后基于和基于梯度的技术的组合为了利用和减少唯一优化技术的缺点。[tra...
Crucially, the ability to estimate the sensitivity with respect to wellbore location in an efficient manner enables the use of gradient-based routines for optimization of complex wellbore designs and configurations. Despite being local, gradient-based techniques are efficient, rely on well-established ...
Here, for a gradient-based optimization technique, the gradient E of the loss function has to be calculated. The corresponding theory is based on the implicit function theorem, see [6, 9], and [1]. An efficient numerical approximation of z in Eq. (1) is also rather complex, for ...
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces. Master status: Dev status: Code quality: Latest versions: Introduction Gradient-Free-Optimizers provides a collection of easy to use optimization techniques, whose ob...
Optimization techniquesThis paper deals with a gradient-based approach to optimizing compressed sensing systems. An alternative measure is proposed for incoherent sparsifying dictionary design. An iterative procedure is developed for searching the optimal dictionary, in which the dictionary update is executed...
Gradient descent mastery: Develop a deep understanding of the essential theory behind the ubiquitous gradient descent approach to optimization, along with hands-on experience applying it using PyTorch and TensorFlow. Latest optimization techniques: Learn about state-of-the-art optimizers, such as ...