Floating Point Precision Error Java
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Java Float Precision 2 Digits
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Java Floating Point Precision Problem
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Floating Point Number Java Example
floating-point numbers if precise computation is required Skip to end of metadata Created by Fred Long, last modified by Arthur Hicken on Nov 03, 2015 Go to start of metadata DeprecatedThis rule may be deprecated and replaced by a similar guideline.06/28/2014 -- Version 1.0 The Java language provides two primitive floating-point types, float and double, which how to set precision for double value in java are associated with the single-precision 32-bit and double-precision 64-bit format values and operations specified by IEEE 754 [IEEE 754]. Each of the floating-point types has a fixed, limited number of mantissa bits. Consequently, it is impossible to precisely represent any irrational number (for example, pi). Further, because these types use a binary mantissa, they cannot precisely represent many finite decimal numbers, such as 0.1, because these numbers have repeating binary representations.When precise computation is necessary, such as when performing currency calculations, floating-point types must not be used. Instead, use an alternative representation that can completely represent the necessary values.When precise computation is unnecessary, floating-point representations may be used. In these cases, you must carefully and methodically estimate the maximum cumulative error of the computations to ensure that the resulting error is within acceptable tolerances. Consider using numerical analysis to properly understand the problem. See Goldberg's work for an introduction to this topic [Goldberg 1991].Noncompliant Code ExampleThis noncompliant code example performs some
DevJolt Awards Channels▼ CloudMobileParallel.NETJVM LanguagesC/C++ToolsDesignTestingWeb DevJolt Awards Eric Bruno Dr. Dobb's Bloggers Bio | Archive Eric Bruno Tweet Permalink Java's Floating-Point (Im)Precision July 24, 2014 Java doesn't store, calculate, or return the exact representation of the actual floating-point value java double precision problem in some cases. I was working with someone recently who told me he java floating point representation was having intermittent issues dealing with double floating-point values in Java. When he added and subtracted dollar amounts double precision floating point java in this financial application, using the Java primitive type double, he would sometimes get unexpected results. For example, the following code shows a sample of some calculations he found troubling (other https://www.securecoding.cert.org/confluence/display/java/NUM04-J.+Do+not+use+floating-point+numbers+if+precise+computation+is+required combinations of values worked fine): double a = 106838.81; double b = 263970.96; double c = 879.35; double d = 366790.80; double total = 0; total += a; total += b; total -= c; total -= d; At the end of this operation, he expected the value 3139.62, but instead was getting 3139.6200000000536. When formatted for display as a dollar amount, http://www.drdobbs.com/jvm/javas-floating-point-imprecision/240168744 it was appearing as expected: "$3139.62." However, when he later inspected the database where he stored results, he noticed the issue. The trouble only came to light when he started seeing "$-0.00" after formatting for currency display. A quick look at the database showed the actual value as -0.000000000053518078857450746. What was going on to cause this, and how could it be fixed? The problem derives from how float and double floating-point values are stored internally by the JVM. Unlike int and long (and other fixed-point types) that are stored as exact binary representations of the numbers they're assigned to, shortcuts are taken with float and double. Internally, Java stores values for these types with an inexact representation, using only a portion of the 64 bits for the significant digits. As a result, Java doesn't store, calculate, or return the exact representation of the actual floating-point value in some cases. This seemingly intermittent behavior can be annoying, as it only becomes apparent with specific combinations of numbers and operations. BigDecimal to the Rescue Fortunately, Java provides a math package, java.math.*, which includes the BigDecima
Oracle Coding Standard for JavaPagesSpace shortcutsDashboardHomeAndroidCC++JavaPerlCERT WebsitePage tree Browse pagesConfigureSpace tools https://www.securecoding.cert.org/confluence/display/java/NUM04-J.+Do+not+use+floating-point+numbers+if+precise+computation+is+required Attachments (0) Page History Restrictions Page Information Resolved comments Link https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html to this Page… View in Hierarchy View Source Export to PDF Export to Word Pages … SEI CERT Oracle Coding Standard for Java 2 Rules Rule 03. Numeric Types and Operations (NUM) Skip to end of banner JIRA links floating point Go to start of banner NUM04-J. Do not use floating-point numbers if precise computation is required Skip to end of metadata Created by Fred Long, last modified by Arthur Hicken on Nov 03, 2015 Go to start of metadata DeprecatedThis rule may be deprecated and replaced by a similar guideline.06/28/2014 floating point precision -- Version 1.0 The Java language provides two primitive floating-point types, float and double, which are associated with the single-precision 32-bit and double-precision 64-bit format values and operations specified by IEEE 754 [IEEE 754]. Each of the floating-point types has a fixed, limited number of mantissa bits. Consequently, it is impossible to precisely represent any irrational number (for example, pi). Further, because these types use a binary mantissa, they cannot precisely represent many finite decimal numbers, such as 0.1, because these numbers have repeating binary representations.When precise computation is necessary, such as when performing currency calculations, floating-point types must not be used. Instead, use an alternative representation that can completely represent the necessary values.When precise computation is unnecessary, floating-point representations may be used. In these cases, you must carefully and methodically estimate the maximum cumulative error of the computations to ensure that the resulting error is within acceptable to
by David Goldberg, published in the March, 1991 issue of Computing Surveys. Copyright 1991, Association for Computing Machinery, Inc., reprinted by permission. Abstract Floating-point arithmetic is considered an esoteric subject by many people. This is rather surprising because floating-point is ubiquitous in computer systems. Almost every language has a floating-point datatype; computers from PCs to supercomputers have floating-point accelerators; most compilers will be called upon to compile floating-point algorithms from time to time; and virtually every operating system must respond to floating-point exceptions such as overflow. This paper presents a tutorial on those aspects of floating-point that have a direct impact on designers of computer systems. It begins with background on floating-point representation and rounding error, continues with a discussion of the IEEE floating-point standard, and concludes with numerous examples of how computer builders can better support floating-point. Categories and Subject Descriptors: (Primary) C.0 [Computer Systems Organization]: General -- instruction set design; D.3.4 [Programming Languages]: Processors -- compilers, optimization; G.1.0 [Numerical Analysis]: General -- computer arithmetic, error analysis, numerical algorithms (Secondary) D.2.1 [Software Engineering]: Requirements/Specifications -- languages; D.3.4 Programming Languages]: Formal Definitions and Theory -- semantics; D.4.1 Operating Systems]: Process Management -- synchronization. General Terms: Algorithms, Design, Languages Additional Key Words and Phrases: Denormalized number, exception, floating-point, floating-point standard, gradual underflow, guard digit, NaN, overflow, relative error, rounding error, rounding mode, ulp, underflow. Introduction Builders of computer systems often need information about floating-point arithm