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Static Analyses of FloatingPoint Operations
 In SAS’01, volume 2126 of LNCS
, 2001
"... Computers manipulate approximations of real numbers, called floatingpoint numbers. The calculations they make are accurate enough for most applications. Unfortunately, in some (catastrophic) situations, the floatingpoint operations lose so much precision that they quickly become irrelevant. In thi ..."
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Computers manipulate approximations of real numbers, called floatingpoint numbers. The calculations they make are accurate enough for most applications. Unfortunately, in some (catastrophic) situations, the floatingpoint operations lose so much precision that they quickly become irrelevant. In this article, we review some of the problems one can encounter, focussing on the IEEE7541985 norm. We give a (sketch of a) semantics of its basic operations then abstract them (in the sense of abstract interpretation) to extract information about the possible loss of precision. The expected application is abstract debugging of software ranging from simple onboard systems (which use more and more ontheshelf microprocessors with floatingpoint units) to scientific codes. The abstract analysis is demonstrated on simple examples and compared with related work. 1
Handbook of FloatingPoint Arithmetic
, 2009
"... REPRESENTING AND MANIPULATING real numbers efficiently is required in many fields of science, engineering, finance, and more. Since the early years of electronic computing, many different ways of approximating real numbers on computers have been introduced. One can cite (this list is far ..."
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Cited by 8 (6 self)
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REPRESENTING AND MANIPULATING real numbers efficiently is required in many fields of science, engineering, finance, and more. Since the early years of electronic computing, many different ways of approximating real numbers on computers have been introduced. One can cite (this list is far
An epsilon Arithmetic for Removing Degeneracies
, 1995
"... Symbolic perturbation by infinitely small values removes degeneracies in geometric algorithms and enables programmers to handle only generic cases: there are a few such cases, whereas there are an overwhelming number of degenerate cases. Current perturbation schemes have limitations, presented below ..."
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Symbolic perturbation by infinitely small values removes degeneracies in geometric algorithms and enables programmers to handle only generic cases: there are a few such cases, whereas there are an overwhelming number of degenerate cases. Current perturbation schemes have limitations, presented below. To overcome them, this paper proposes to use an ffl arithmetic, i.e. to represent in an explicit way infinitely small numbers, and to define arithmetic operations (+; \Gamma; ; =; !; =) on them. 1 Introduction Handling all degeneracies is a burden when implementing geometric algorithms: there are a few generic cases, but numerous degeneracies, for instance in 2D alignments of more than two points, cocircularity of more than three points, intersection of more than two lines in a point, parallelism between lines or between a sweeping line and segments for some algorithms: : : Symbolic perturbation removes degeneracies, and so programmers, like theoreticians, can ideally focus on the tre...
The Appearance of Big Integers in Exact Real Arithmetic based on Linear Fractional Transformations
 In Proc. Foundations of Software Science and Computation Structures (FoSSaCS '98), volume 1378 of LNCS
, 1997
"... . One possible approach to exact real arithmetic is to use linear fractional transformations to represent real numbers and computations on real numbers. In this paper, we show that the bit sizes of the (integer) parameters of nearly all transformations used in computations are proportional to the nu ..."
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. One possible approach to exact real arithmetic is to use linear fractional transformations to represent real numbers and computations on real numbers. In this paper, we show that the bit sizes of the (integer) parameters of nearly all transformations used in computations are proportional to the number of basic computational steps executed so far. Here, a basic step means consuming one digit of the argument(s) or producing one digit of the result. 1 Introduction Linear Fractional Transformations (LFT's) provide an elegant approach to real number arithmetic [8, 16, 11, 14, 12, 6]. Onedimensional LFT's x 7! ax+c bx+d are used as digits and to implement basic functions, while twodimensional LFT's (x; y) 7! axy+cx+ey+g bxy+dx+fy+h provide binary operations such as addition and multiplication, and can be combined to infinite expression trees denoting transcendental functions. In Section 2, we present the details of the LFT approach. This provides the background for understanding the r...
How Many Argument Digits are Needed to Produce n Result Digits?
 In RealComp '98 Workshop (June 1998 in Indianapolis), volume 24 of Electronic Notes in Theoretical Computer Science
, 1999
"... In the LFT approach to Exact Real Arithmetic, we study the question how many argument digits are needed to produce a certain number of result digits. We present upper and lower bounds for many simple functions and operations, and for exponential and square root. 1 Introduction In this paper, we wor ..."
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In the LFT approach to Exact Real Arithmetic, we study the question how many argument digits are needed to produce a certain number of result digits. We present upper and lower bounds for many simple functions and operations, and for exponential and square root. 1 Introduction In this paper, we work in an approach to Exact Real Arithmetic where real numbers are represented as potentially infinite streams of information units, called digits. Hence, an algorithm to compute a certain expression over real numbers is a device that reads some input streams and produces an output stream. Algorithms like this never terminate, but are considered as satisfactory if they produce any desired number of output digits in finite time, i.e., from a finite number of input digits by a finite number of internal operations. The (time) efficiency of a real number algorithm indicates how much time T (n) it takes to produce n result digits. It clearly depends on the number of input digits needed to produce ...
Big Integers and Complexity Issues in Exact Real Arithmetic
 In Third Comprox workshop
, 1998
"... One possible approach to exact real arithmetic is to use linear fractional transformations to represent real numbers and computations on real numbers. We show how to determine the digits that can be emitted from a transformation, and present a criterion which ensures that it is possible to emit a di ..."
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One possible approach to exact real arithmetic is to use linear fractional transformations to represent real numbers and computations on real numbers. We show how to determine the digits that can be emitted from a transformation, and present a criterion which ensures that it is possible to emit a digit. Using these results, we prove that the obvious algorithm to compute n digits from the application of a transformation to a real number has complexity O(n 2 ), and present a method to reduce this complexity to that of multiplying two n bit integers. 1 Introduction Linear Fractional Transformations (LFT's) provide an elegant approach to real number arithmetic [5,14,9,12,10,4]. Onedimensional LFT's x 7! ax+c bx+d are used as digits and to implement basic unary functions, while twodimensional LFT's (x; y) 7! axy+cx+ey+g bxy+dx+fy+h provide binary operations such as addition and multiplication, and can be combined to obtain infinite expression trees denoting transcendental functions...
What About the Natural Numbers
 Computer Languages
, 1989
"... A prime concern in the design of any general purpose programming language should be the ease and safety of working with natural numbers, particularly in conjunction with discrete data structures. This theme of commitment to the naturals as the basic numeric data type is explored in the context of a ..."
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A prime concern in the design of any general purpose programming language should be the ease and safety of working with natural numbers, particularly in conjunction with discrete data structures. This theme of commitment to the naturals as the basic numeric data type is explored in the context of a lazy functional language. NonTitle Keywords: structural correspondence, numeric types, total functions, closed systems, functional programming, lazy evaluation.
On Termination of Logic Programs With Floating Point computations
, 2002
"... Numerical computations form an essential part of almost any realworld program. Traditional approaches to termination of logic programs are restricted to domains isomorphic to N , more recent works study termination of integer computations. Termination of computations involving real numbers is cumbe ..."
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Numerical computations form an essential part of almost any realworld program. Traditional approaches to termination of logic programs are restricted to domains isomorphic to N , more recent works study termination of integer computations. Termination of computations involving real numbers is cumbersome and counterintuitive due to rounding errors and implementation conventions. We present a novel technique that allows us to prove termination of such computations.
Real Number Computation through Gray Code Embedding
, 2000
"... We propose an embedding G of the unit open interval to the set f0; 1g ! ?;1 of infinite sequences of f0; 1g with at most one undefined element. This embedding is based on Gray code and it is a topological embedding with a natural topology on f0; 1g ! ?;1 . We also define a machine called an IM2 mach ..."
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We propose an embedding G of the unit open interval to the set f0; 1g ! ?;1 of infinite sequences of f0; 1g with at most one undefined element. This embedding is based on Gray code and it is a topological embedding with a natural topology on f0; 1g ! ?;1 . We also define a machine called an IM2 machine (indeterministic multihead type 2 machine) which input/output sequences in f0; 1g ! ?;1 , and show that the computability notion induced on real functions through the embedding G is equivalent to the one induced by the signed digit representation and Type2 machines. We also show that basic algorithms can be expressed naturally with respect to this embedding.
Constructive Analysis with Witnesses
"... Contents 1. Real Numbers 3 2 3 1.2. Reals, Equality of Reals 5 1.3. The Archimedian Axiom 6 1.4. Nonnegative and Positive Reals 6 1.5. Arithmetical Functions 7 1.6. Comparison of Reals 8 1.7. NonCountability 10 1.8. Cleaning of Reals 11 2. Sequences and Series of Real Numbers 11 2.1. Completenes ..."
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Contents 1. Real Numbers 3 2 3 1.2. Reals, Equality of Reals 5 1.3. The Archimedian Axiom 6 1.4. Nonnegative and Positive Reals 6 1.5. Arithmetical Functions 7 1.6. Comparison of Reals 8 1.7. NonCountability 10 1.8. Cleaning of Reals 11 2. Sequences and Series of Real Numbers 11 2.1. Completeness 11 2.2. Limits and Inequalities 13 2.3. Series 13 2.4. Redundant Dyadic Representation of Reals 14 2.5. Convergence Tests 15 2.6. Reordering Theorem 17 2.7. The Exponential Series 18 3. The Exponential Function for Complex Numbers 21 4. Continuous Functions 23 4.1. Suprema and In ma 24 4.2. Continuous Functions 25 4.3. Application of a Continuous Function to a Real 27 4.4. Continuous Functions and Limits 28 4.5. Composition of Continuous Functions 28 4.6. Properties of Continuous Functions 29 4.7. Intermediate Value Theorem 30 4.8. Continuity of Functions with More Than One Variable 32 5. Dierentiation 33 5.1. Derivatives 33 5.2. Bounds on the Slope 33 5.3. Properties of Derivatives 34 5