blocks|key|672821|text|double是一种非常快速的浮点数据类型，在许多芯片组上以非常低的级别实现。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|672822|它的精确度足以满足许多应用:例如，测量太阳到冥王星的距离到最近的厘米！|672823|在考虑迁移到更精确的数据类型时，总是要权衡性能，因为后者会慢得多，而且您最喜欢的数学库可能不支持它们。请记住，程序的输出是输入质量的函数。|672824|最后一句话，千万不要用双精度来表示现金数量！|672825|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

A double is a remarkably fast floating point data type implemented at a very low level on many chipsets.

Its precision is sufficient for very many applications: e.g. measuring the distance of the sun to Pluto to the nearest centimetre!

Always a performance trade-off when thinking about moving to a more precise data type as the latter will be much slower and your favourite mathematical libraries may not support them. Remember that the outputs of your program are a function of the quality of the inputs.

As a final remark, never use a double to represent cash quantities though!

blocks|key|1812871|text|BigDecimal|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1812872|1812873|不变的、任意精度的有符号十进制数。BigDecimal由任意精度的整数、未缩放值和32位整数小数位数组成。如果为零或正数，则小数位数为小数点右侧的位数。如果为负数，则将该数字的未缩放值乘以10，为该刻度的负幂。因此，BigDecimal表示的数字的值是(unscaledValue×10-scale)。|blockquote|1812874|1812875|1812876|Double具有一定的精度。|style|CODE|1812877|编辑：+BigDecimal是一个真实的对象，而不是一个原始对象。因此，它抽象了数字表示，并且不受物理(读内存)限制。礼节:+|BOLD|1812878|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.oracle.com/javase/7/docs/api/java/math/BigDecimal.html^0|0|A|0|0|0|0|0|0|0|6|0|0|3|1N|3|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|D|3|-4|5|6|7|12|8|@]|9|@]|C|$]]|$1|E|3|F|5|G|7|13|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|14|8|@]|9|@]|C|$]]|$1|I|3|-4|5|6|7|15|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|16|8|@$A|17|B|18|L|M]]|9|@]|C|$]]|$1|N|3|O|5|6|7|19|8|@$A|1A|B|1B|L|P]|$A|1C|B|1D|L|P]]|9|@]|C|$]]|$1|Q|3|-4|5|6|7|1E|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]]]

<a href="http://docs.oracle.com/javase/7/docs/api/java/math/BigDecimal.html" rel="nofollow noreferrer">BigDecimal</a>

<blockquote>
 Immutable, arbitrary-precision signed decimal numbers. A BigDecimal consists of an arbitrary precision integer unscaled value and a 32-bit integer scale. If zero or positive, the scale is the number of digits to the right of the decimal point. If negative, the unscaled value of the number is multiplied by ten to the power of the negation of the scale. The value of the number represented by the BigDecimal is therefore (unscaledValue × 10-scale).
</blockquote>

A <code>Double</code> has a certain precision.

EDITED: BigDecimal is a real object, not a primitive one. Thus, it abstracts numerical representation and is not bound by physical (read memory) restrictions.
courtesy : <a href="https://stackoverflow.com/users/219324/max-leske">Max Leske</a>

blocks|key|4103328|text|double有8个字节来表示值，其精度限制在15位十进制数字，参见http://en.wikipedia.org/wiki/IEEE_754-1985。BigDecimal精度实际上是无限的，因为它基于任意长度的整数数组。尽管使用double的操作比使用BigDecimal快得多，但是这种数据类型不应该用于精确的值，例如货币。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4103329|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/IEEE_754-1985^0|X|16|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

double has 8 bytes to represent the value, its precision is limited to 15 decimal digits, see <a href="http://en.wikipedia.org/wiki/IEEE_754-1985" rel="nofollow">http://en.wikipedia.org/wiki/IEEE_754-1985</a>. BigDecimal precision is de facto unlimited since it is based on an int array of arbitrary length. Though operations with double are much faster than with BigDecimal this data type should never be used for precise values, such as currency.

blocks|key|1812949|text|在BigDecimal上执行运算时，结果中的位数通常会大于任一操作数。这有两个主要影响：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1812950|1812951|除非代码强制定期舍入，否则随着数字越来越长，BigDecimal上的操作将变得越来越慢。|ordered-list-item|1812952|固定大小的容器不可能大到足以容纳BigDecimal，因为填满各自容器的两个值之间的许多操作将产生过长的结果，无法放入该大小的容器中。|1812953|1812954|float和double可以很快，而BigDecimal则不能，其根本原因是它们被定义为在任何计算中去除尽可能多的精度，以便产生与原始操作数相同大小的容器中的结果。这使它们能够使用固定大小的容器，而不必担心后续操作变得越来越慢。|1812955|顺便说一句，BigDecimal速度慢的另一个主要原因(虽然不太重要)是，值是使用二进制格式的尾数而是十进制指数来表示的。因此，任何需要调整操作数精度的操作都必须在一个非常昂贵的“规范化”步骤之前进行。如果任何给定值只有一个表示，则该类型可能更容易使用，因此将123.456与0.44相加得到123.5而不是123.500，但将123.500归一化为123.5将需要比相加123.456和0.044更多的计算量；此外，如果将该结果与小数点后有三个有效数字的另一个数字相加，则在较早的相加之后执行的归一化将增加执行下一个数字所需的时间。|1812956|entityMap^0|1|A|0|0|M|A|0|G|A|0|0|0|5|6|6|I|A|0|6|A|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|V|8|@]|D|@]|E|$]]|$1|G|3|H|5|I|7|W|8|@$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|J|3|K|5|I|7|Z|8|@$9|10|A|11|B|C]]|D|@]|E|$]]|$1|L|3|-4|5|6|7|12|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|13|8|@$9|14|A|15|B|C]|$9|16|A|17|B|C]|$9|18|A|19|B|C]]|D|@]|E|$]]|$1|O|3|P|5|6|7|1A|8|@$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1D|8|@]|D|@]|E|$]]]|R|$]]

When operations are performed on <code>BigDecimal</code>, the number of digits in the result will frequently be larger than either operand. This has two major effects:

<ol>
<li>Unless code forces periodic rounding, operations on <code>BigDecimal</code> will get slower and slower as the numbers get longer and longer.</li>
<li>No fixed-size container can possibly be big enough to accommodate a <code>BigDecimal</code>, since many operations between two values which filled up their respective containers would yield a result too long to fit into a container of that size.</li>
</ol>

The fundamental reason that <code>float</code> and <code>double</code> can be fast, while <code>BigDecimal</code> cannot, is that they are defined as lopping off as much precision as is necessary in any calculation so as to yield a result which will fit in the same size of container as the original operands. This enables them to use fixed-size containers, and not have to worry about succeeding operations becoming progressively slower.

Incidentally, another major (though less fundamental) reason that <code>BigDecimal</code> is slow is that values are represented using a binary-formatted mantissa but a decimal exponent. Consequently, any operations which would require adjusting the precision of their operands must be preceded by a very expensive "normalization" step. The type might be easier to work with if any given value had exactly one representation, and thus adding 123.456 to 0.044 yielded 123.5 rather than 123.500, but normalizing 123.500 to 123.5 would require much more computation than adding of 123.456 and 0.44; further, if that result is added to another number with three significant figures after the decimal point, the normalization performed after the earlier addition would increase the time required to perform the next one.

blocks|key|1814856|text|使用double计算要比使用BigDecimal快得多，因为它是原生类型，您还可以在指尖上进行全面的数学运算。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1814857|如果你需要执行非常精确的计算，BigDecimal是很棒的，但你要付出代价，比如不能很容易地访问在BigDecimal上实际工作的平方根函数，而且通常计算速度要慢得多。|1814858|entityMap^0|2|6|E|A|0|F|A|1D|A|0^^$0|@$1|2|3|4|5|6|7|J|8|@$9|K|A|L|B|C]|$9|M|A|N|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]]|D|@]|E|$]]|$1|H|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|I|$]]

Calculating with <code>double</code> is much faster than with <code>BigDecimal</code> as it's a primative type you also have the full range of mathematical operations at your fingertips. 

<code>BigDecimal</code> is great if you need to perform very precise calculations but you pay a price for that such as not having easy access to a square root function that actually works on a <code>BigDecimal</code> and generally much slower calculations.

blocks|key|4103348|text|因为我遇到的问题是，双精度值的精确值是有限的，所以当你使用精度超过限制时，值将被舍入，但在大小数的情况下，你不会面临这个问题|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4103349|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|$]]

As I faced problem with Double is its precision value is limited , so when you use precision more than limitation the value will be rounded off but in case of Big Decimal you will not face this problem

blocks|key|4103493|text|根据定义，double具有固定的精度(以2为基数的53位)。它的主要优点是它非常快，因为在实践中，基本操作完全在硬件中实现。如果这样的精度和/或基数2不适合您的应用程序，您可以使用任意精度的算法。BigDecimal是标准的，使用基数10。但是现在有GNU+MPFR+Java+bindings+(我还没有尝试过)，所以你可以使用GNU+MPFR库来进行计算，它使用基数2，应该比BigDecimal快得多。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4103494|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/kframework/mpfr-java|1|https://www.mpfr.org/^0|5|6|2Q|A|5A|A|3H|M|0|4L|8|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@$9|Q|A|R|B|C]|$9|S|A|T|B|C]|$9|U|A|V|B|C]]|D|@$9|W|A|X|1|Y]|$9|Z|A|10|1|11]]|E|$]]|$1|F|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|G|$H|$5|I|J|K|E|$L|M]]|N|$5|I|J|K|E|$L|O]]]]

By definition, <code>double</code> has a fixed precision (53 digits in base 2). Its main advantage is that it is very fast since in practice the basic operations are entirely implemented in hardware. If such a precision and/or base 2 are not adapted to your application, you can use an arbitrary-precision arithmetic. <code>BigDecimal</code> is standard and uses base 10. However there now are <a href="https://github.com/kframework/mpfr-java" rel="nofollow noreferrer">GNU MPFR Java bindings</a> (I haven't tried), so that you can do your computations with the <a href="https://www.mpfr.org/" rel="nofollow noreferrer">GNU MPFR</a> library, which uses base 2 and should be significantly faster than <code>BigDecimal</code>.

I would like to know the exact difference between <code>BigDecimal</code> and <code>double</code>. I know that <code>BigDecimal</code> is more precise than <code>double</code> and you should use the former for calculations.

Why is it more precise? Why isn't it possible to customize <code>double</code> so that it works like <code>BigDecimal</code>? Or are there any advantages to calculating with <code>double</code>?

Why is BigDecimal more precise than double?

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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我想知道BigDecimal和double之间的确切区别。我知道BigDecimal比double更精确，你应该使用前者来计算。为什么它更精确？为什么不能像BigDecimal一样定制double呢？或者，使用double进行计算有什么好处吗

问为什么BigDecimal比double更精确？
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