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相关概念视频

Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

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Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Step-Growth Polymerization: Overview01:03

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Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
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Updated: Jun 5, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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随机初级-双元混合梯度算法与自适应式步骤大小.

Antonin Chambolle1,2, Claire Delplancke3, Matthias J Ehrhardt4

  • 1CEREMADE, Université Paris-Dauphine, Place du Maréchal De Lattre De Tassigny, 75775 Paris, France.

Journal of mathematical imaging and vision
|December 13, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了随机初级-双元混合梯度 (SPDHG) 算法的适应性步骤大小,改进了大规模的凸式优化. 新的自适应式SPDHG (A-SPDHG) 确保了融合,并为增强性能提供了实用的参数选择.

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科学领域:

  • 优化算法 优化算法
  • 计算科学 计算科学
  • 应用数学 应用数学 应用数学

背景情况:

  • 随机原始-双重混合梯度 (SPDHG) 算法由于其可扩展性,被广泛用于大规模的凸式优化.
  • SPDHG的收依赖于初级和双级尺寸的产品的上限.
  • 在实际应用中,为SPDHG选择最佳的步骤尺寸比仍然是一个挑战.

研究的目的:

  • 为凸优化开发一种具有适应性步骤大小的新型原始-双元算法.
  • 介绍一个适应式SPDHG (A-SPDHG) 算法的一般类别.
  • 提供系统的策略来选择SPDHG中的步骤大小,以确保融合.

主要方法:

  • 提出了一个适应性SPDHG (A-SPDHG) 算法的一般类.
  • 在弱假设下证明了A-SPDHG的收性质.
  • 为A-SPDHG制定了具体的参数更新策略.

主要成果:

  • 证明了拟议的A-SPDHG算法的融合.
  • 通过数值示例验证了适应性方案的有效性.
  • 展示了计算机断层扫描重建中的成功应用.

结论:

  • 针对SPDHG算法的拟议的自适应步骤大小策略增强了趋同性和实际适用性.
  • A-SPDHG为大规模的凸式优化问题提供了强大的解决方案.
  • 开发的方法为步骤大小选择提供了一个系统的方法,克服了以前的局限性.