Linked from

The 31 pages that link to Floating-point arithmetic, each with the reason it gives.

• Real numberCompared with: Computers cannot store most real numbers exactly, so calculations use finite approximations.

• Numerical analysisRelated: Its representation limits shape the errors that practical algorithms must manage.

• Graphics processing unitRelated: Graphics and scientific workloads rely heavily on floating-point operations.

• Binary numberRelated: Finite binary precision explains rounding and representation errors in computation.

• Numerical stabilityNarrower topic: Its rounding behavior supplies the perturbations that stable algorithms must manage.

• Condition numberRelated: Rounding errors are amplified when a problem has a large condition number.

• Significant figuresCompared with: Machine rounding errors arise from representation limits, not measurement reporting conventions.

• Scientific notationRelated: Its representation is closely related to scientific notation, with finite precision and range.

• High-performance computingRelated: HPC simulations rely on floating-point operations and must account for rounding error.

• Bisection methodRelated: Rounding can affect midpoint calculations and sign tests near a computed root.

• FortranRelated: Scientific Fortran programs depend on floating-point results and their precision limits.

• AdditionRelated: Computer addition can round results when values exceed the representation’s precision.

• Scientific computingRelated: Rounding and representation errors can change computed scientific results.

• RoundingRelated: Its finite representations require rounding during many computations.

• Symbolic computationCompared with: Rounding error distinguishes floating-point calculations from exact symbolic manipulation.

• Computer algebra systemCompared with: It contrasts with the exact symbolic representations many computer algebra operations preserve.

• Decimal expansionRelated: Computer arithmetic must handle finite approximations rather than arbitrary infinite decimal expansions.

• Direct methodRelated: Rounding means a finite sequence of operations may not yield an exact solution in computer arithmetic.

• Interval arithmeticRelated: Rounding in floating-point calculations motivates enclosing results with intervals.

• Root-finding algorithmRelated: Rounding and underflow can alter computed function values and stopping decisions.

• Powers of tenRelated: Powers of ten help describe decimal formats, while binary powers govern many machine representations.

• Fixed-point arithmeticCompared with: Its variable exponent trades fixed-point's uniform spacing for much greater dynamic range.

• Polynomial evaluationRelated: Rounding can make a computed polynomial value differ from its exact value.

• Secant methodRelated: Roundoff and nearly equal function values can affect the computed secant slope.

• Horner's methodRelated: Rounding during Horner evaluation affects the accuracy of its computed value.

• Division by zeroRelated: Its standards specify computational outcomes that differ from ordinary arithmetic's undefined quotient.

• Konrad ZuseRelated: The Z3 used binary floating-point arithmetic to represent a broad range of numerical values.

• Floating-point unitNarrower topic: The unit is hardware for carrying out these operations.

• 64-bit computingCompared with: A 64-bit floating-point value has a different precision and range trade-off than a 64-bit integer.

• IBM 7030 StretchRelated: Scientific workloads depended on the numerical operations Stretch was built to perform quickly.

• Numerical methods for ordinary differential equationsRelated: Roundoff can affect accuracy and stability even when the mathematical step formula is sound.