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Finite-precision direct factorizations and least squares — Examples
1 · Prerequisites
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Direct Matrix Factorisations: LU, Cholesky and QR
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite-precision direct factorizations and least squares
- Foundations of the Real Numbers for Analysis
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Matrix Norms, Condition Numbers and Numerical Stability
- Metric Spaces
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Series: Convergence and the Nonnegative Tests
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
These computations exhibit a nonzero rounded Cholesky residual, loss of a Gram-matrix pivot under binary64 normal-equation formation, forward sensitivity in an ill-conditioned design, compensation after cancellation, one exact TwoSum recovery, and explicit subnormal and overflow range failures. The TwoSum example checks all six rounded nodes and binary64 range success, and its general binary exact-transform theorem is proved locally. Householder-dependent examples use the locally repaired stability results.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
Subnormal and overflow branches return range failure
Example
Use binary64 and the no-subnormal success policy. Put N_min=2^-1022 and the least positive subnormal 2^-1074. Inputs N_min and -(N_min+2^-1074) are normal, but their exact sum is -2^-1074, so the only tree sum in pairwise summation returns range-failure. Also N_max+N_max overflows at the root and returns range-failure. These are policy branches, not claims that IEEE hardware lacks gradual underflow.
Facts & Assumptions
Given: binary64 normal endpoints and the no-subnormal range rule.
Operationwise real and complex floating point arithmetic with rounded square roots and fused multiply-add: nonzero subnormal and overflow results return range-failure.
Balanced pairwise summation with range-failure status: each internal sum is rounded and the first failure is returned.
Verification
The exact first sum is 2^-1022-(2^-1022+2^-1074)=-2^-1074, a nonzero subnormal; [F1] therefore returns range-failure.
The exact sum N_max+N_max exceeds the largest finite normal magnitude, so the root triggers the same status under [F2].
Neumaier summation recovers a low order term after cancellation
Example
In binary64 RNE sum (1,2^-53,-1). Ordinary left-to-right summation rounds 1+2^-53 to 1 by ties-to-even, then returns zero after adding -1. Neumaier step 2 gives s_1=1, t_2=1, q_2=0, a_2=2^-53, c_2=2^-53; step 3 gives t_3=0, q_3=1, a_3=0, c_3=2^-53. The final result is 2^-53, the exact sum. The finite error identity accounts for each branch correction and the final rounding.
Facts & Assumptions
Given: binary64 round-to-nearest, ties-to-even.
Neumaier compensated summation with range-failure status: the method branches on the larger magnitude and returns the rounded sum plus compensation.
Kahan and Neumaier summation satisfy explicit finite data-dependent error bounds: its identity includes each executed correction and final rounding.
Operationwise real and complex floating point arithmetic with rounded square roots and fused multiply-add: binary64 addition is round-to-nearest, ties-to-even.
Verification
The value 2^-53 is half an ulp at 1, so 1+2^-53 rounds to 1. Ordinary summation then returns 0.
Neumaier step 2 selects |s_1|>=|x_2| and gives t_2=1,q_2=0,a_2=2^-53,c_2=2^-53; step 3 gives t_3=0,q_3=1,a_3=0,c_3=2^-53.
The final exact addition 0+2^-53 is 2^-53, which is the exact sum; [F2] records this correction without claiming uniform relative accuracy.
A rounded two by two Cholesky factor and its residual
Example
In binary64 round-to-nearest, ties-to-even, let For every nonzero real , so is positive definite. The rounded recurrence gives , , exactly, and . Since is irrational and every binary64 number is rational, . Therefore The successful factor satisfies the backward bound, but its residual is not zero.
Facts & Assumptions
Given: the real symmetric matrix and binary64 RNE arithmetic.
Failure-aware rounded Cholesky factorization: successful factorization uses the rounded pivot and square-root recurrence.
A successful rounded Cholesky factorization has componentwise and normwise backward error bounds: success gives with the stated bound.
Operationwise real and complex floating point arithmetic with rounded square roots and fused multiply-add: positive square roots and basic operations are rounded to nearest, ties to even.
Verification
The displayed quadratic form proves positive definiteness. The first pivot is , , and the second pivot is exactly, so the procedure succeeds through the square-root step.
Every binary64 value is rational, whereas sqrt(3/4)=sqrt(3)/2 is irrational; hence l-hat_22^2!=3/4. Multiplication of the displayed factor gives exactly the stated nonzero E, to which [F2] applies.
TwoSum recovers a rounded away low part
Example
In binary64 nearest-even arithmetic, take and . The call to TwoSum error-free transformation succeeds and returns . It preserves exactly although the leading rounded sum loses the low part.
Facts & Assumptions
Given: binary64 nearest-even arithmetic with , , , and .
TwoSum error-free transformation evaluates the six separately rounded operations displayed in its definition.
TwoSum returns the exact rounding error of a floating point sum proves for every successful call in a binary format of precision at least two.
Operationwise real and complex floating point arithmetic with rounded square roots and fused multiply-add requires zero or normal exact intermediates and nearest-even rounding.
Verification
The spacing above is , so is the midpoint between and . The even-significand endpoint is ; hence . The next exact operands give , , , and . Each is representable, so each subtraction is exact.
The final exact intermediate is , hence . The first exact sum and every later nonzero exact intermediate ( or ) are normal and within range; the zero intermediates are allowed by [F3]. Thus the call succeeds, its format satisfies [F2], and exactly, also directly verified by these outputs.
A full rank least squares problem where rounded normal equations lose the pivot
Example
In binary64 RNE take A=((1,1),(0,2^-27)). Its determinant 2^-27 is nonzero. The exact Gram matrix is A^T A=((1,1),(1,1+2^-54)). The spacing above 1 is 2^-52, so the midpoint is 1+2^-53; 1+2^-54 lies strictly below it and rounds to 1. Thus the formed Gram matrix has equal rows and is singular. Reduced QR works directly on A and avoids forming this rounded Gram matrix; the computed stability statement remains conditional on its own hypotheses.
Facts & Assumptions
Given: binary64 RNE and A=((1,1),(0,2^-27)).
Reduced QR over the reals solves full-column-rank least squares without squaring the condition number: exact reduced QR solves full-rank least squares without forming the Gram matrix.
Computed reduced QR least squares is backward stable for full column rank: the computed guarantee is conditional on the listed QR and solve premises.
Operationwise real and complex floating point arithmetic with rounded square roots and fused multiply-add: binary64 operations use round-to-nearest, ties-to-even.
Verification
The determinant is 2^-27, while direct multiplication gives (A^T A)_22=1+2^-54.
Since 2^-54<2^-53, the exact entry is strictly below the RNE midpoint above 1 and rounds to 1. The rounded Gram matrix has equal rows and is singular.
The exact result [F1] acts on the full-rank A without a Gram formation. The computed conclusion [F2] applies only if its separate successful-step hypotheses hold.
An ill conditioned design with large forward sensitivity
Example
Let A=diag(1,2^-40), with condition number 2^40. For b=(1,0)^T the unique solution is x=(1,0)^T; for b-tilde=(1,2^-40)^T it is x-tilde=(1,1)^T. The relative data change is 2^-40 while the relative solution change is 1. Thus backward stability does not imply small forward error for an ill-conditioned design.
Facts & Assumptions
Given: the diagonal design and right-hand sides.
Reduced QR over the reals solves full-column-rank least squares without squaring the condition number: full-column-rank least squares has a unique minimizer.
Computed reduced QR least squares is backward stable for full column rank: its guarantee controls perturbations of data under its hypotheses.
Verification
Direct multiplication by A^-1=diag(1,2^40) gives x=A^-1b=(1,0)^T and x-tilde=A^-1b-tilde=(1,1)^T; uniqueness follows from [F1].
The input difference and x norm are both measured against unit vectors: ||b-tilde-b||_2/||b||_2=2^-40 and ||x-tilde-x||_2/||x||_2=1.
For every , , with equality at , so [F4] gives . Likewise , with equality at , hence . Thus [F3] gives . The exact data calculation proves order-one forward change under a relative right-hand-side perturbation; backward stability [F2] alone is not a forward-accuracy guarantee.
Sources
- Fundamentals of Numerical Computation, floating-point arithmetic
- Stuart and Voss, Matrix Analysis and Algorithms, Warwick lecture notes
- Higham, The Accuracy of Floating Point Summation
- Ogita, Rump, and Oishi, Accurate Sum and Dot Product
- Fundamentals of Numerical Computation, Cholesky factorization
- Fundamentals of Numerical Computation, normal equations
- Fundamentals of Numerical Computation, QR factorization and least squares