sublinear

简明释义

英[ˌsʌbˈlɪniər]美[ˌsʌbˈlɪniər]

adj. 亚线性的，次线性的；放在一行字下面的

英英释义

单词用法

同义词

反义词

例句

1.In this paper, we prove a sufficent and necessary condition of existence for the solution of a kind of sublinear singular bound ary value problem.

证明了一类含脉冲次线性奇异边值问题解存在的充要性条件，推广了以前的相应结果。

2.With the concavity and integrability of sublinear terms near zero, the symmetry results for a class of sublinear elliptic equations are given by making use of the moving-plane method.

摘要本文利用次线性项在零点附近的凹性和可积性，用移动平面法给出了一类次线性椭圆方程正解的对称性。

3.The oscillation of a class of second order strongly sublinear delay differential equations is discussed. Three new theorems are established.

讨论了一类二阶强次线性时滞微分方程解的振动性质，建立了三个新的振动性定理。

4.The oscillation of a class of second order strongly sublinear delay differential equations is discussed. Three new theorems are established. The results generalize and improve some known ones.

讨论了一类二阶强次线性时滞微分方程解的振动性质，建立了三个新的振动性定理。推广和改进了已知的一些结果。

5.Until now, all the results on central limit theorems under sublinear expectations require that the sequence of random variables is independent and identically distributed.

本文的目的是探究次线性期望理论中一个重要的结果：中心极限定理。

6.The oscillation of a class of second order strongly sublinear delay differential equations is discussed. Three new theorems are established. The results generalize and improve some known ones.

讨论了一类二阶强次线性时滞微分方程解的振动性质，建立了三个新的振动性定理。推广和改进了已知的一些结果。

7.The existence of multiple solutions is obtained for Neumann problem of sublinear elliptic equations by the minimax methods in the critical point theory.

用临界点理论中的极小极大方法得到了次线性椭圆方程Neumann问题多重解的存在性。

8.The oscillation for solutions of the class of the second order strongly sublinear differential equation are discussed and three new oscillation theorems are obtained.

讨论了一类二阶强次线性微分方程解的振动性质，获得了三个新的振动性定理，推广和改进了相关文献的结果。

9.The existence of positive solution is proved for a class of sublinear fractional differential equations where the nonlinear terms subject to the power functions.

证明了一类非线性项受幂函数控制的次线性分数微分方程的正解存在性。

10.The algorithm runs in sublinear 次线性 time, making it efficient for large datasets.

该算法在次线性时间内运行，使其适用于大数据集。

11.The performance of the new software is sublinear 次线性 when processing queries.

新软件在处理查询时的性能是次线性的。

12.In our experiment, we observed that the search time was sublinear 次线性 compared to traditional methods.

在我们的实验中，我们观察到搜索时间与传统方法相比是次线性的。

13.We implemented a sublinear 次线性 algorithm for our data mining project to enhance speed.

我们为数据挖掘项目实施了一种次线性算法，以提高速度。

14.Using a sublinear 次线性 approach can significantly reduce the computational resources needed.

使用次线性的方法可以显著减少所需的计算资源。

作文

In the realm of computer science and mathematics, the term sublinear refers to a type of algorithmic performance that grows slower than linear time. This means that as the size of the input data increases, the time it takes to complete the operation increases at a rate less than proportional to the input size. Understanding sublinear complexities is crucial for developing efficient algorithms, especially when dealing with large datasets. For example, consider a search algorithm that can find an item in a list of millions of entries without needing to examine each entry individually. Such an algorithm may operate in sublinear time by leveraging techniques like hashing or binary search, which significantly reduces the number of comparisons needed. The significance of sublinear algorithms becomes even more apparent in the context of big data. With the exponential growth of data in various fields such as finance, healthcare, and social media, traditional linear algorithms become increasingly impractical. A linear search through a billion records would take a considerable amount of time, whereas a sublinear approach could provide results almost instantaneously. This efficiency is not merely a convenience; it can lead to breakthroughs in real-time data analysis and decision-making processes. Moreover, the concept of sublinear extends beyond just time complexity. It can also apply to space complexity, where an algorithm uses memory in a manner that is less than linear relative to the input size. For instance, certain streaming algorithms can process data streams in sublinear space, allowing them to analyze large volumes of data without needing to store everything in memory. This is particularly useful in scenarios where memory resources are limited or when working with data that is generated continuously. Additionally, the implications of sublinear performance are not limited to theoretical discussions. In practical applications, businesses and organizations that utilize sublinear algorithms can achieve faster processing times and lower operational costs. For example, search engines use sublinear methods to index vast amounts of web content efficiently, enabling users to retrieve information quickly. Similarly, machine learning models often incorporate sublinear techniques to optimize their training processes, allowing them to learn from extensive datasets without being bogged down by computational overhead. In conclusion, grasping the concept of sublinear performance is essential for anyone involved in technology and data science. As we continue to navigate an increasingly data-driven world, the ability to implement sublinear algorithms will be a key factor in achieving efficiency and effectiveness in various applications. By focusing on sublinear approaches, we can unlock new possibilities and enhance our capabilities in processing and analyzing data. The future of algorithm design will undoubtedly revolve around maximizing sublinear efficiencies to keep pace with the ever-growing demands of information technology and data analytics.

在计算机科学和数学领域，术语sublinear指的是一种算法性能，其增长速度低于线性时间。这意味着随着输入数据大小的增加，完成操作所需的时间以低于与输入大小成比例的速率增加。理解sublinear复杂性对于开发高效算法至关重要，尤其是在处理大型数据集时。例如，考虑一个搜索算法，它可以在数百万个条目的列表中找到一个项目，而无需逐个检查每个条目。这样的算法可以通过利用哈希或二分搜索等技术以sublinear时间运行，从而显著减少所需的比较次数。sublinear算法的重要性在大数据背景下变得更加明显。随着金融、医疗保健和社交媒体等各个领域数据的指数增长，传统的线性算法变得越来越不切实际。对十亿条记录进行线性搜索将耗费相当多的时间，而sublinear方法则可以几乎瞬间提供结果。这种效率不仅仅是一种便利；它可以在实时数据分析和决策过程中带来突破。此外，sublinear的概念不仅限于时间复杂度。它也可以应用于空间复杂度，其中算法使用的内存相对于输入大小低于线性。例如，某些流算法可以在sublinear空间中处理数据流，使其能够分析大量数据，而无需将所有内容存储在内存中。这在内存资源有限或处理持续生成的数据时尤为有用。此外，sublinear性能的影响不仅限于理论讨论。在实际应用中，利用sublinear算法的企业和组织可以实现更快的处理时间和更低的运营成本。例如，搜索引擎使用sublinear方法高效地索引大量网页内容，使用户能够快速检索信息。同样，机器学习模型通常结合sublinear技术来优化其训练过程，使它们能够在没有计算开销的情况下从广泛的数据集中学习。总之，掌握sublinear性能的概念对于任何参与技术和数据科学的人来说都是必不可少的。随着我们继续在日益数据驱动的世界中航行，实现sublinear算法的能力将成为在各种应用中实现效率和有效性的关键因素。通过关注sublinear方法，我们可以解锁新的可能性，并增强我们处理和分析数据的能力。算法设计的未来无疑将围绕最大化sublinear效率，以跟上信息技术和数据分析日益增长的需求。