blocks|key|3817338|text|如果有人说“这个算法运行在O(n)时间内”，他说的是速度。如果有人说“这个算法在O(n)空间中运行”，他指的是内存。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3817339|如果他只是说“这个算法是O(n)"，他通常是在谈论速度(尽管如果他在讨论内存时说了这句话，他可能是在谈论内存)。|3817340|如果你不确定别人说的是哪一个，去问他。|3817341|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

If someone says "This algorithm runs in O(n) time", he's talking about speed. If someone says "This algorithm runs in O(n) space", he's talking about memory.

If he just says "This algorithm is O(n)", he's usually talking about speed (though if he says it during a discussion about memory, he's probably talking about memory).

If you're not sure which one someone's talking about, ask him.

blocks|key|3817196|text|简而言之:你有“大O在空间”和“大O在时间”。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3817197|长答案:+Big+O只是一个符号，你可以在任何你想要的上下文中使用它。|3817198|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Short answer : you have 'Big O in space" and "Big O in time".

Long answer: Big O is just a notation, you can use it in whatever context you want.

blocks|key|1469455|text|大O只是一个数学工具，可以用来描述任何函数。通常人们用它来描述速度，但它也可以用来描述内存使用情况。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1469456|此外，当我们使用大O表示时间时，我们通常不直接谈论时钟周期。取而代之的是，我们计算“基本操作”(隐含地假设需要恒定的周期数)。|1469457|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Big O is just a mathematical tool that can be used to describe any function. Usually people use it to describe speed, but it can just as well be used to describe memory usage.

Also, when we use Big O for time, we're usually not talking directly about clock cycles. Instead, we count "basic operations" (that are implicitly assumed to take a constant number of cycles).

blocks|key|723133|text|通常情况下，它是操作的数量，这可以转换为速度。通常，算法在速度上的差异比内存使用上的差异更大。但是，在适当的时候，您将看到O()表示法用于内存使用。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|723134|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Typically it's the number of operations, which translates to speed. Usually, algorithms differ more in speed than in memory usage. However, you will see the O() notation used for memory use, when appropriate.

blocks|key|3817377|text|大O实际上只是基于输入增长的复杂性增长的度量。两种算法的执行时间都是O(n)，但它们的增长与输入的增长是线性的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3817378|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Big O is really just a measure of the growth of complexity based on growth of input.
Two algorithms with are both O(n) may execute in vastly different times but their grown is linear with relation to the growth of the input.

blocks|key|2409378|text|大O可以用来描述任意两个量之间的关系。虽然它通常只在计算机科学中使用，但这个概念也可能适用于其他领域，如物理学。例如，无论天线的形状如何，为了在一定距离上产生单位强度的信号，必须投入到给定尺寸的天线中的功率为O(d%5E2)。如果天线的尺寸相对于距离较大，则所需强度的增加可能是线性的，甚至是次线性的，而不是二次的，但随着距离的增加，二次项将占主导地位。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2409379|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Big-O can be used to describe the relationship between any two quantities. Although it is generally only used in computer science, the concept may also be applicable in other fields like physics. For example, the amount of power that must be put into an antenna of a given size to yield a unit-strength signal at some distance is O(d^2), regardless of the antenna shape. If the size of the antenna is large relative to the distance, the increase in strength required may be linear or even sub-linear rather than quadratic, but as the distance gets larger, the quadratic term will dominate.

blocks|key|723272|text|大O等是用来衡量某事物的增长的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|723273|当有人说某个东西是O(N)，那么它的增长速度不会比线性速率快。如果某个东西是Ω(N%5E2)，那么它的增长速度不会慢于二次速率。当某个东西是Θ(2%5EN)的时候，它会以指数的速度增长。|offset|length|style|CODE|723274|这个东西可以是算法的时间要求。它也可以是算法的空间，即内存需求。它也可以是几乎任何东西，既不与空间也不与时间相关。|723275|例如，一些大规模并行算法通常测量它可以在其上运行的处理器数量的可扩展性。特定的算法可以在O(N%5E2)时间内在O(N)处理器上运行。另一种算法可以在O(N)时间内在O(N%5E2)处理器上运行。|723276|相关问题|723277|723278|Big-oh+vs+big-theta|unordered-list-item|723279|723280|另请参阅|723281|723282|Wikipedia/Big+O+Notation|723283|723284|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/3230122/big-oh-vs-big-theta/|1|http://en.wikipedia.org/wiki/Big_O_notation^0|0|9|4|12|6|1W|6|0|0|18|6|1I|4|21|4|29|6|0|0|0|0|J|0|0|0|0|0|0|O|1|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|19|8|@$D|1A|E|1B|F|G]|$D|1C|E|1D|F|G]|$D|1E|E|1F|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|1G|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|1H|8|@$D|1I|E|1J|F|G]|$D|1K|E|1L|F|G]|$D|1M|E|1N|F|G]|$D|1O|E|1P|F|G]]|9|@]|A|$]]|$1|L|3|M|5|6|7|1Q|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|1R|8|@]|9|@]|A|$]]|$1|O|3|P|5|Q|7|1S|8|@]|9|@$D|1T|E|1U|1|1V]]|A|$]]|$1|R|3|-4|5|6|7|1W|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1X|8|@]|9|@]|A|$]]|$1|U|3|-4|5|6|7|1Y|8|@]|9|@]|A|$]]|$1|V|3|W|5|Q|7|1Z|8|@]|9|@$D|20|E|21|1|22]]|A|$]]|$1|X|3|-4|5|6|7|23|8|@]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|24|8|@]|9|@]|A|$]]]|Z|$10|$5|11|12|13|A|$14|15]]|16|$5|11|12|13|A|$14|17]]]]

Big O and others are used to measure the growth of something.

When someone says that something is <code>O(N)</code>, then that thing grows no faster than linear rate. If something is <code>Ω(N^2)</code>, then that thing grows no slower than quadratic rate. When something is <code>Θ(2^N)</code>, then that thing grows at an exponential rate.

What that thing is can be the time requirement for an algorithm. It can also be the space i.e. memory requirement for an algorithm. It can also be pretty much anything, related to neither space nor time.

For example, some massively parallel algorithms often measure the scalability in the number of processors that it can run on. A particular algorithm may run on <code>O(N)</code> processors in <code>O(N^2)</code> time. Another algorithm may run on <code>O(N^2)</code> processors in <code>O(N)</code> time.

<h3>Related questions</h3>

<ul>
<li><a href="https://stackoverflow.com/questions/3230122/big-oh-vs-big-theta/">Big-oh vs big-theta</a></li>
</ul>

<h3>See also</h3>

<ul>
<li><a href="http://en.wikipedia.org/wiki/Big_O_notation" rel="nofollow noreferrer">Wikipedia/Big O Notation</a></li>
</ul>

blocks|key|3817412|text|虽然算法通常会按时竞争，但我通常会假设任何O语句都是时间。但是，比较空间也是完全有效的。O可以用于任何测量-我们通常使用速度，因为它通常是最重要的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3817413|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

While normally algorithms compete on time, then I would normally assume that any O statement was time. However, it's perfectly valid to also compare space. O can be used for any measurement- we just normally use speed because it's normally the most important.

I often here people talk about Big O which measures algorithms against each other

Does this measure clock cycles or space requirements.

If people want to contrast algorithms based on memory usage what measure would they use

Does Big O Measure Memory Requirments Or Just Speed?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我经常在这里谈论Big O，它衡量彼此之间的算法这是否衡量时钟周期或空间需求。如果人们想要对比基于内存使用的算法，他们会使用什么度量标准

问大O是衡量内存需求，还是仅仅衡量速度？
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问大O是衡量内存需求，还是仅仅衡量速度？EN

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问大O是衡量内存需求，还是仅仅衡量速度？
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