reducibly

简明释义

英[rɪˈdjuːsəblɪ]美[rɪˈduːsəblɪ]

可缩小的（reducible 的变体）

可还原的（reducible 的变体）

可简化的（reducible 的变体）

英英释义

单词用法

同义词

反义词

例句

1.In order to fit in with the needs of Chinese market, the printing machines introduced from the foreign countries must frequently be reducibly designed or enlargedly designed.

为了适应我国市场的需求，往往要对引进的印刷机械进行缩放设计。

2.In order to fit in with the needs of Chinese market, the printing machines introduced from the foreign countries must frequently be reducibly designed or enlargedly designed.

为了适应我国市场的需求，往往要对引进的印刷机械进行缩放设计。

3.The research indicates that the phenomenon can be reducibly analyzed through various lenses.

研究表明，这一现象可以通过不同的视角可约化地分析。

4.In mathematics, a function is reducibly defined when it can be simplified to a more basic form.

在数学中，当一个函数可以简化为更基本的形式时，它被称为可约化地定义。

5.The problem can be solved reducibly by breaking it down into smaller parts.

这个问题可以通过将其分解为更小的部分来可约化地解决。

6.Her argument was reducibly clear, making it easy for everyone to understand.

她的论点可约化地清晰，使每个人都能轻松理解。

7.The algorithm works reducibly, allowing for efficient processing of large datasets.

该算法可约化地工作，允许高效处理大数据集。

作文

In the realm of mathematics and computer science, the concept of reducibility plays a crucial role in understanding complex problems. A problem is said to be reducibly solvable if it can be transformed into a simpler version of itself, which is easier to solve. This idea is not only applicable in theoretical contexts but also has practical implications in various fields such as optimization, algorithms, and even artificial intelligence.To illustrate this concept, consider the classic problem of sorting a list of numbers. At first glance, sorting might seem like a daunting task, especially when dealing with large datasets. However, by recognizing that sorting can be reducibly broken down into smaller, manageable subproblems, we can apply efficient algorithms such as merge sort or quicksort. These algorithms take advantage of the fact that if we can sort smaller sections of the list, we can then combine those sorted sections to achieve a fully sorted list. Thus, the original problem is reducibly simplified into smaller problems that are easier to tackle.Another example can be found in the field of artificial intelligence, particularly in machine learning. When training a model, one often encounters a high-dimensional dataset that can be overwhelming to process. By employing dimensionality reduction techniques, such as Principal Component Analysis (PCA), we can reducibly transform the dataset into a lower-dimensional space without losing significant information. This not only makes the data easier to visualize but also improves the efficiency of the learning algorithms applied to it.The concept of being reducibly solvable is not limited to technical fields; it can also be applied to everyday problem-solving scenarios. For instance, consider a person trying to organize a large event. The tasks involved may seem insurmountable at first—booking venues, catering, sending invitations, and managing schedules can quickly become overwhelming. However, by breaking down the planning process into smaller, reducibly defined tasks, such as creating a checklist, setting deadlines, and delegating responsibilities, the individual can manage the workload more effectively. Each small task contributes to the overall goal of organizing the event, making the entire process less stressful and more achievable.Furthermore, understanding the principle of reducibility encourages a mindset of systematic thinking. It teaches us that many complex issues can be approached with a strategy of simplification. When faced with a challenging situation, whether in academics, work, or personal life, recognizing the potential to reducibly divide the problem into smaller components can lead to clearer solutions and better outcomes.In conclusion, the notion of being reducibly solvable is a powerful concept that transcends disciplines. It enables us to tackle complexity by breaking it down into simpler elements, whether in mathematics, computer science, or daily life. By applying this approach, we can enhance our problem-solving skills and make progress in a structured and effective manner. Embracing the idea of reducibility allows us to navigate challenges with confidence, knowing that every complex problem has the potential to be simplified into manageable parts.

在数学和计算机科学领域，可约性概念在理解复杂问题时起着至关重要的作用。如果一个问题可以被转化为一个更简单的版本，从而更容易解决，那么这个问题就被称为可约地可解的。这个理念不仅适用于理论背景，还在优化、算法甚至人工智能等多个领域具有实际意义。为了说明这个概念，考虑经典的排序问题。乍一看，排序似乎是一项艰巨的任务，尤其是在处理大型数据集时。然而，通过认识到排序可以被可约地分解成更小、更易管理的子问题，我们可以应用高效的算法，如归并排序或快速排序。这些算法利用了这样一个事实：如果我们可以对列表的较小部分进行排序，那么我们就可以将这些已排序的部分组合起来，从而实现整个列表的完全排序。因此，原始问题被可约地简化为更容易处理的小问题。另一个例子可以在人工智能领域找到，特别是在机器学习中。当训练模型时，人们常常会遇到一个高维数据集，这可能会让人感到不知所措。通过采用降维技术，如主成分分析（PCA），我们可以可约地将数据集转换为低维空间，而不会丢失重要信息。这不仅使数据更容易可视化，而且提高了应用于该数据集的学习算法的效率。可约可解的概念不仅限于技术领域；它还可以应用于日常问题解决场景。例如，考虑一个人试图组织一场大型活动。涉及的任务乍一看可能显得不可逾越——预定场地、餐饮、发送邀请函和管理日程安排很快就会变得令人不知所措。然而，通过将规划过程分解为更小的、可约定义的任务，例如创建清单、设定截止日期和委派责任，这个人可以更有效地管理工作量。每个小任务都为组织活动的整体目标做出了贡献，使整个过程变得不那么紧张，更容易实现。此外，理解可约性的原则鼓励系统思维的心态。它教会我们，许多复杂问题可以通过简化的策略来处理。当面临挑战性情境时，无论是在学业、工作还是个人生活中，意识到有可能可约地将问题分解为更小的组成部分，可以导致更清晰的解决方案和更好的结果。总之，可约可解的观念是一个强大的概念，超越了学科界限。它使我们能够通过将复杂性分解为更简单的元素来应对挑战，无论是在数学、计算机科学还是日常生活中。通过应用这种方法，我们可以增强我们的解决问题的能力，以结构化和有效的方式取得进展。接受可约性这一理念使我们能够自信地应对挑战，因为我们知道每个复杂问题都有可能被简化为可管理的部分。