reducibleness

简明释义

英[rɪˈdjuːsəblnəs]美[rɪˈduːsəblnəs]

n. 可还原性

英英释义

单词用法

同义词

反义词

例句

1.The embedded system, as the center of the application and emphasizing the reducibleness of the bulk and functions, is the special system which finishes the function of controlling or stakeout.

嵌入式系统以应用为中心，强调体积和功能的可裁减性，是以完成控制、监视等功能为目标的专用系统；

2.The embedded system, as the center of the application and emphasizing the reducibleness of the bulk and functions, is the special system which finishes the function of controlling or stakeout.

嵌入式系统以应用为中心，强调体积和功能的可裁减性，是以完成控制、监视等功能为目标的专用系统；

3.The team evaluated the reducibleness of various chemical compounds to determine their 可约性 for industrial applications.

团队评估了各种化合物的可约性，以确定它们在工业应用中的可约性。

4.The mathematician discussed the reducibleness of complex equations, emphasizing their 可约性 in simpler forms.

这位数学家讨论了复杂方程的可约性，强调它们在更简单形式中的可约性。

5.In computer science, the reducibleness of algorithms is crucial for optimizing performance, as it determines 可约性是否能提高效率。

在计算机科学中，算法的可约性对优化性能至关重要，因为它决定了可约性是否能提高效率。

6.The physicist's research focused on the reducibleness of physical laws, which can often be simplified into more manageable equations, illustrating their 可约性.

这位物理学家的研究集中在物理定律的可约性上，这些定律通常可以简化为更易管理的方程，说明它们的可约性。

7.In game theory, understanding the reducibleness of strategies helps players identify optimal moves based on their 可约性.

在博弈论中，理解策略的可约性帮助玩家根据其可约性识别最佳行动。

作文

In the realm of mathematics and computer science, the concept of reducibleness plays a crucial role in understanding complex problems and their solvability. The term reducibleness refers to the ability of a problem to be transformed or reduced into another problem that is already known to be solvable or easier to solve. This characteristic is essential for researchers and practitioners who aim to devise efficient algorithms and solutions for intricate challenges.To illustrate the significance of reducibleness, consider the famous P versus NP problem, which questions whether every problem whose solution can be verified quickly by a computer can also be solved quickly by a computer. If a problem is reducible to an NP-complete problem, it implies that solving it could potentially lead to breakthroughs in various fields, including cryptography, optimization, and artificial intelligence.Moreover, the reducibleness of a problem can help in classifying it within different complexity classes. For example, if a problem can be reduced to a known NP-complete problem, it is likely that it is also NP-complete. This classification aids in understanding the inherent difficulty of problems and guides researchers in focusing their efforts on those that are more tractable.In practical applications, reducibleness is often utilized in algorithm design. By identifying a problem's reducibleness, developers can create algorithms that leverage existing solutions to tackle new challenges. For instance, when faced with a scheduling problem, one might reduce it to a simpler version, such as a single-resource allocation problem, which has known solutions. This process not only saves time but also enhances the efficiency of problem-solving approaches.Furthermore, the concept of reducibleness extends beyond theoretical frameworks; it also finds relevance in real-world scenarios. In operations research, for example, businesses often encounter complex logistical challenges. By recognizing the reducibleness of these problems to well-studied models, organizations can implement strategies that optimize resources and improve overall efficiency.In conclusion, the reducibleness of a problem is a fundamental aspect of both theoretical and applied disciplines. It serves as a bridge between complex issues and their manageable counterparts, enabling researchers and professionals to navigate the vast landscape of problem-solving. Understanding reducibleness empowers individuals to approach challenges systematically, leading to innovative solutions and advancements across various fields. As we continue to explore the intricacies of mathematics and computer science, the importance of reducibleness will undoubtedly remain a pivotal focus in our quest for knowledge and efficiency.

在数学和计算机科学领域，reducibleness的概念在理解复杂问题及其可解性方面发挥着至关重要的作用。术语reducibleness指的是一个问题能够被转化或简化为另一个已知可解决或更易解决的问题的能力。这一特性对于研究人员和从业者来说至关重要，他们旨在为复杂挑战设计高效的算法和解决方案。为了说明reducibleness的重要性，考虑著名的P与NP问题，它质疑每一个可以被计算机快速验证其解决方案的问题是否也可以被计算机快速解决。如果一个问题可以被简化为一个NP完全问题，这意味着解决它可能会在包括密码学、优化和人工智能等多个领域带来突破。此外，问题的reducibleness可以帮助将其分类到不同的复杂性类别中。例如，如果一个问题可以简化为一个已知的NP完全问题，那么它很可能也是NP完全的。这种分类有助于理解问题的固有难度，并指导研究人员将精力集中在那些更易处理的问题上。在实际应用中，reducibleness常常被用于算法设计。通过识别问题的reducibleness，开发人员可以创建利用现有解决方案来应对新挑战的算法。例如，当面临调度问题时，人们可能会将其简化为一个单资源分配问题，这个问题有已知的解决方案。这个过程不仅节省了时间，还提高了问题解决方法的效率。此外，reducibleness的概念超越了理论框架；它在现实世界场景中也具有相关性。在运筹学中，例如，企业经常遇到复杂的物流挑战。通过识别这些问题的reducibleness为经过充分研究的模型，组织可以实施优化资源和改善整体效率的策略。总之，问题的reducibleness是理论和应用学科的一个基本方面。它作为复杂问题与可管理对手之间的桥梁，使研究人员和专业人士能够在解决问题的广阔领域中航行。理解reducibleness使个人能够系统地应对挑战，从而导致创新解决方案和各个领域的进步。随着我们继续探索数学和计算机科学的复杂性，reducibleness的重要性无疑将继续成为我们追求知识和效率的关键焦点。