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Minimax approximation and Remez algorithms
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Binary Operations, Monoids, Groups and Subgroups
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- 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
- Countability and Uncountability
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Fundamental Trigonometric Identities
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial approximation and Chebyshev interpolation
- Polynomial interpolation and remainder
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
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- Rⁿ as a Normed Space; Vector-Valued Functions
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- Series: Convergence and the Nonnegative Tests
- Sine, Cosine, and the Definition of Pi
- Subspaces, Products, and Quotients
- Suprema and Infima
- The Derivative and the Mean Value Theorems
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
For a continuous real target on a nondegenerate compact interval, the page proves existence of a best polynomial approximant, the n+2 alternating-extrema criterion for nonzero error, and uniqueness. It defines finite-budget polynomial Remez exchange with a distinct ordered reference set, an invertible alternation system, and an exact global-extrema oracle; a returned exact equioscillation certificate proves optimality. No iterative convergence claim is made.
The rational family uses real reduced numerator and denominator degrees at most m and n, a strictly positive continuous weight, and a denominator that is strictly positive on the interval. Its defect is d and its alternation count is m+n+2-d; the normal case d=0 gives m+n+2, while defective cases use the smaller count. The rational exchange procedure has an explicit nonlinear-solver, singularity, defect, oracle, and finite-budget status contract. No m+n+2 claim is copied into a defective case.
3 · Logical flowchart
4 · Definitions, theorems and proofs
Best uniform polynomial error on a compact interval
Definition
Let with , and let denote the real-valued continuous functions on . For set . For , let be the restrictions to of real polynomials of degree at most , including the zero polynomial, and define When , this agrees with the published definition Best uniform polynomial error and Chebyshev interpolation operators.
These quantities are finite and well-defined. The interval is compact by For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent, so each continuous difference has a finite maximum absolute value. The polynomial space contains , hence the set of errors is nonempty and bounded below by ; completeness of (Complete ordered field (least-upper-bound property)) supplies its infimum.
A polynomial with the sign pattern of the active extrema
Statement
Let with , let be continuous, and let . Put and . If there are no increasing points of with alternating signs, then there is a real polynomial of degree at most such that for every .
Facts & Assumptions
Given: with , a continuous real function on , , and an integer .
Intervals of : the nine order-convex forms, nondegeneracy, and length: is a closed bounded interval in .
For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: a closed bounded nonempty subset of is compact, and every continuous real function on it attains its extrema.
The reverse triangle inequality, The triangle inequality, and Basic properties of the absolute value: , , and for real .
Proof
Continuity and attainment in [F2] show that is nonempty and compact: the maximum of is attained and , while each level set is closed in the compact interval [F1]. If one of or is empty, take or according to the sign of the nonempty set; this has degree zero and the required sign.
Suppose both sets are nonempty. Their product is a closed bounded subset of . For , the reverse and ordinary triangle inequalities in [F3] give , so the distance function is continuous. By [F2] it attains a minimum separation . This minimum is positive because a minimizing pair at distance zero would give a point in both disjoint sets. Starting at the leftmost point of , repeatedly take the first point of the opposite-sign set to the right and the last current-sign point before it. These extrema exist because the level sets are compact, and each transition gives an open gap of length at least containing no point of . The gaps are pairwise disjoint subintervals of , so any transitions satisfy ; hence there are finitely many transitions, each with a distinct ordered gap.
If there were at least transitions, choosing one extremal point from each successive sign run would give ordered alternating points, contrary to the hypothesis. Thus the number of transitions is at most . Choose one point in each transition gap and set , where is chosen so that has the sign of on the first run. The polynomial has degree , has no zero on , and changes sign exactly when the extremal sign changes. Hence on the entire extremal set.
Alternating polynomial signs force the Haar zero-count contradiction
Statement
Let , let be a nonzero real polynomial of degree at most , and let be real numbers. The values cannot all be nonzero with alternating signs.
Facts & Assumptions
Given: , a nonzero real polynomial of degree at most , and with each .
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function: every real polynomial function is continuous on its domain.
Intermediate value theorem, by bisection with a canonical left-half rule: a continuous function on takes every value between and : if a continuous real function has endpoint values of opposite signs on a closed interval, it has a zero in the open interval.
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero real polynomial of degree at most has at most distinct real roots.
Proof
Suppose that the nonzero values alternate in sign. By [F1], is continuous on each . Since the endpoint values are nonzero and have opposite signs, [F2] gives a root for every . These open intervals are pairwise disjoint, so the are distinct real roots.
The distinct roots from step 1.1 contradict [F3], because is nonzero and has degree at most . Therefore the assumed alternating nonzero values cannot occur.
Polynomial coefficients are bounded by values at fixed distinct nodes
Statement
Let and let be distinct real numbers. For , define If has degree at most , then Consequently, writing , every coefficient satisfies The empty product convention gives when .
Facts & Assumptions
Given: An integer , distinct real nodes , and a real polynomial of degree at most .
Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials: a polynomial of degree at most is a finite coefficient sum .
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero polynomial of degree at most has at most distinct real roots.
Proof
Each denominator is nonzero, so every is a polynomial of degree at most and is for and otherwise. The polynomial has degree at most and vanishes at the distinct nodes; [F2] makes it the zero polynomial. This also covers , when .
Comparing the coefficients of in the identity of step 1.1 gives . The triangle inequality yields the stated coefficient bound.
Alternating interpolation weights annihilate lower-degree polynomials
Statement
Let and let be real nodes. Set and . If is a real polynomial of degree at most , then If, for one fixed , the values are all nonnegative, then is the zero polynomial.
Facts & Assumptions
Given: , strictly increasing real nodes , and .
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero polynomial of degree at most cannot vanish at distinct nodes; a nonzero polynomial of degree at most cannot vanish at distinct nodes.
Proof
Each . The Lagrange polynomial has degree and values for and otherwise. Thus has degree at most and agrees with at all nodes. By [F1], , so .
The coefficient of in is , while that coefficient in is . Comparing the coefficients in from step 1.1 proves the displayed sum identity.
The sign of is , since exactly the factors with are negative. Hence all terms have one common weak sign when . Their sum is by step 2.1, so every term is , and all values vanish.
Since has degree at most , its distinct roots in step 3.1 force by [F1].
Reduced real rational representations are unique up to scale
Statement
Call a pair of real polynomials reduced when and no nonconstant polynomial divides both entries. We identify and as the same rational function exactly when . Every quotient with nonzero denominator has a reduced representation, and any two reduced representations of the same nonzero rational function differ by a common nonzero real scalar. The zero rational function has reduced pair up to a nonzero scalar. For , define , unchanged by appending zero coefficients. If is a nonempty interval, two reduced representations of the same rational function with denominators strictly positive on and coefficient norm are equal.
Facts & Assumptions
Given: Real polynomials with .
Bézout identity and the Euclidean algorithm for polynomials over a field: polynomial division over gives a monic gcd and polynomials with ; divides both inputs, and every common divisor divides .
Proof
For a nonzero quotient, let from [F1] and put , . Then implies , so no nonconstant polynomial divides both and . For the zero quotient, and division by the monic gives ; scaling by gives . Any reduced pair representing zero has zero numerator and a nonzero constant denominator.
Suppose are nonzero reduced representations. By the stated equality convention, . The Bézout identity from step 1.1, multiplied by , shows divides ; multiplying the same identity by shows . Write and . The nonzero real leading coefficients of and have nonzero product, so ; cross-multiplication then gives and hence . If were nonconstant, it would divide both and , contradicting reduction. Thus is a nonzero real constant. The zero case is the reduced pair already identified in step 1.1.
Since is nonempty, choose . If the common scalar from step 2.1 is , then and both denominators are positive, so . Homogeneity of the coefficient norm and give , so the numerators agree as well.
Weighted rational minimax families and defect
Definition
Let with , let , and let with for every . A normalized reduced pair is a pair of real polynomials such that on , , , and have no nonconstant common divisor, and The zero rational function is represented by . By Reduced real rational representations are unique up to scale, each rational function admissible under these conditions has exactly one normalized reduced pair. Define and The family is nonempty because it contains . For each admissible , on makes continuous by Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function, so is continuous and bounded on compact . The infimum is therefore finite, bounded below by zero, and exists as a real number by completeness of .
For in reduced form, put For , define and hence . The integer is the defect. A candidate is normal when and defective when . The alternation count associated with the candidate is ; it equals only in the normal case.
The Remez alternation linear system has a unique solution
Statement
Let , let with , let be any function, and let be distinct nodes in . There is a unique pair satisfying
Facts & Assumptions
Given: , with , a real function defined at the nodes, and ordered distinct nodes in .
Alternating polynomial signs force the Haar zero-count contradiction: a nonzero polynomial of degree at most cannot take nonzero alternating signs at ordered real nodes.
Rank-nullity: : for a linear map between finite-dimensional vector spaces, the domain dimension is the sum of its kernel dimension and image dimension.
Best uniform polynomial error on a compact interval: is the space of restrictions to of real polynomials of degree at most .
Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials: every real polynomial of degree at most is represented by a coefficient sum .
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero real polynomial of degree at most has at most distinct real roots.
Proof
Define the linear map by . Choose a polynomial representative of using [F3] and [F4]. If and , then vanishes at all distinct nodes, so [F5] forces and hence . If , then are nonzero alternating values, so is nonzero and [F1] rules this out. Thus .
The restrictions of span by [F3] and [F4]. If a linear combination of them restricts to zero on , its representing polynomial vanishes at . These are distinct roots, so [F5] makes that polynomial zero; the coefficient representation then makes every coefficient zero. Thus the restrictions are linearly independent and form a basis, so . Therefore is a basis of the domain, while the standard coordinate vectors form a basis of the codomain; both dimensions are . By [F2] and step 1.1, has image dimension and is therefore bijective. Its equation has exactly one solution.
Best uniform polynomial approximants exist
Statement
Let with , let , and let . There is a polynomial such that .
Facts & Assumptions
Given: with , an integer , and .
Best uniform polynomial error on a compact interval: consists of restrictions of real polynomials of degree at most , and .
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function: every polynomial function and every finite sum or scalar multiple of continuous functions is continuous.
For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: closed bounded subsets of are compact, and continuous real-valued functions on nonempty compact subsets attain minima and maxima.
Polynomial coefficients are bounded by values at fixed distinct nodes: on any fixed distinct nodes, each polynomial coefficient is a fixed linear combination of the sampled values.
Proof
Write for and set . For , compactness of and [F2] give . For every , the triangle inequality gives ; taking suprema and then exchanging yields . Thus is continuous. The sublevel set is closed and contains .
If , then . For , take ; for , take distinct nodes , . Each , so [F4] bounds every coefficient uniformly on . Hence is bounded.
By [F3], the nonempty closed bounded set is compact, so the continuous function attains a minimum on . Since , ; every coefficient vector outside has . Therefore is the minimum over all of , which by [F1] is . The attaining polynomial is the required .
Exact polynomial equioscillation certifies best approximation
Statement
Let , let with , and let . If and there are ordered nodes in and a sign such that then no polynomial in has smaller uniform error than . Thus the displayed residual values are an a posteriori optimality certificate.
Facts & Assumptions
Given: , a nondegenerate closed interval , a continuous real function , a polynomial of degree at most , its positive error , and the stated ordered alternation nodes in .
Best uniform polynomial error on a compact interval: the comparison class is and its error is the uniform norm on that interval.
Alternating polynomial signs force the Haar zero-count contradiction: no nonzero polynomial of degree at most can have nonzero alternating signs at ordered nodes.
Proof
Suppose has . At a node with , the strict inequality gives , so has the same nonzero sign as .
The polynomial has degree at most and, by step 1.1, has nonzero alternating signs at ordered nodes. This contradicts [F2]. Therefore no such exists, and is a best approximant.
Finite-budget polynomial Remez exchange with an exact extrema oracle
Definition
Inputs. A nondegenerate interval , a degree , a continuous real function on , an integer exchange budget , and an initial reference with . Also supply an exact extrema oracle for a polynomial candidate . On each call it returns one of:
- alternation, only when and the returned ordered distinct nodes lie in and satisfy for one fixed sign ;
- maximum, only when , and ; or
- inconclusive, when neither exact certificate can be returned.
Procedure. For , solve the unique real linear system for and , using The Remez alternation linear system has a unique solution. Query the oracle for . If it returns alternation or maximum, return exact, since either response certifies zero uniform error. If it returns alternation with , return certified; the optimality claim is discharged by Exact polynomial equioscillation certifies best approximation. If it returns inconclusive, return oracle-inconclusive. If it returns maximum with and , return certified, because the solved equations make an alternating set and the oracle certifies the global norm. If , return zero-level-no-exchange. If , return budget-exhausted with and the current reference before attempting an exchange.
In the remaining case, let . Find the smallest index for which and replacing by preserves a strictly increasing reference tuple under these tests: for , require ; for , require ; and for , require . Thus a global maximum at an endpoint may replace the corresponding endpoint reference node, while distinctness and strict ordering are retained. Replace by and continue. If no such index exists, return no-order-preserving-exchange.
Outputs. The procedure returns an exact best-approximation certificate, an exact zero-error candidate, or one of the explicit inconclusive, zero-level, no-exchange, or budget statuses. It asserts no convergence of the iterates. Any implementation must ensure each exact solve and oracle call terminates.
The rational first-variation space has dimension set by defect
Statement
Let be nonzero, with its reduced pair and defect , and put . Then For with the convention , the same equality holds.
Facts & Assumptions
Given: A nondegenerate interval , nonnegative degrees , and a normalized admissible rational function with the reduced convention of Weighted rational minimax families and defect.
Weighted rational minimax families and defect: for , are coprime, , , and ; for , .
Bézout identity and the Euclidean algorithm for polynomials over a field: if are coprime, there are polynomials with .
Rank-nullity: : domain dimension is kernel dimension plus image dimension for a linear map of finite-dimensional vector spaces.
Over an integral domain, degrees add under multiplication of nonzero polynomials: for nonzero polynomials over , product degrees add.
Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials: has basis and dimension .
Proof
Define by . It is linear. If , [F1] gives , so and its image is exactly .
Suppose . If , then . Multiply the Bézout identity in [F2] by : , so for . Substitution into and cancellation of the nonzero polynomial give . Conversely, every lies in the kernel.
The degree bounds on and , together with [F4], are equivalent to ; the zero polynomial is included. Thus the kernel has basis and dimension . By [F3] and [F5], the image dimension is .
For all , the degree of is at most by [F1] and [F4]. Hence the image is a subspace of , whose dimension is by [F5]. Its dimension from step 3.1 is also , so the two spaces are equal.
Best weighted rational approximants exist
Statement
For with , , , and a strictly positive continuous weight , there is such that
Facts & Assumptions
Given: A nondegenerate compact interval , integers , , and on .
Weighted rational minimax families and defect: candidates have reduced degrees at most , a denominator normalized by coefficient Euclidean norm , and error .
Reduced real rational representations are unique up to scale: for a nonempty interval, two reduced representations of the same rational function with denominators positive on and coefficient norm are equal.
For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: continuous functions on nonempty compact intervals attain extrema; closed bounded subsets of finite-dimensional Euclidean space are compact and continuous functions on them attain minima.
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero polynomial has finitely many roots in .
A real polynomial vanishing at is divisible by : if a polynomial vanishes at , it is divisible by .
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function and Continuity of at a point of and on : the - condition, its agreement with at a limit point, and continuity at an isolated point: polynomial functions and products of continuous functions are continuous.
Polynomial coefficients are bounded by values at fixed distinct nodes: for fixed distinct nodes, bounded polynomial values give a fixed bound on all coefficients.
Over an integral domain, degrees add under multiplication of nonzero polynomials: degrees add for products of nonzero real polynomials.
Intermediate value theorem, by bisection with a canonical left-half rule: a continuous function on takes every value between and : a continuous real function on an interval takes every value between its endpoint values.
Proof
Let and . Positivity of follows because the continuous positive function attains its minimum on compact by [F3]. In the coefficient space for pairs of degrees at most and an extra scalar , define by , on , , and for every . The triple lies in , so is nonempty.
The coefficient norm equation, the interval for , and each pointwise inequality defining are closed conditions on the finite coefficient vector, so is closed. Its denominator coefficients have norm , and for every , on . The coefficient norm bound gives , hence is uniformly bounded. For , evaluation at one node bounds the sole coefficient of . For , evaluate at the distinct nodes ; each value is bounded by , so [F7] bounds every coefficient. Thus is bounded.
By [F3], is compact, so its coordinate function has a minimum at some . For every admissible rational with error at most , [F1] supplies a normalized positive reduced representing pair, unique by [F2]; with this triple belongs to . Candidates with larger error exceed . Therefore .
For the minimizing pair, is a nonzero nonnegative polynomial because its coefficient norm is . Its zero set in is finite by [F4]. The defining inequality of and give for every . At any zero of , repeated use of [F5] writes with and, if , writes with . If and , then [F8] and [F4] give on a punctured one-sided neighborhood of inside . By continuity in [F6] and , is bounded below by a positive constant on a sufficiently small such neighborhood, so is unbounded as from within , contradicting the displayed bound. Hence is divisible by the factor of at every interval zero; this is automatic if . Thus every interval zero of cancels from the quotient.
For each interior zero of , its multiplicity is even: an odd multiplicity would make change sign across , contradicting on . Form from the factors at interior zeros (where is even), at if it is a zero, and at if it is a zero. Then on and off the finite zero set of . Step 3.2 shows that divides both and . Write and . The exact multiplicities included in make nonzero at every zero of ; elsewhere, and , so . Since is continuous by [F6], positivity off the finite zero set makes it nonnegative at each removed zero; its nonvanishing there makes it positive on all of . At every point outside those zeros, divide the defining inequality by to obtain . Thus there. The quotient and are continuous on by [F6], so the same bound holds at the cancelled zeros by continuity. Reduce using [F2]. The reduced denominator still has no zero on because it divides ; it is continuous by [F6] and has constant sign by [F9] (otherwise the intermediate value theorem would give a zero). A common sign change makes it positive, and a further positive scaling gives coefficient norm . The resulting pair is admissible and represents the same quotient, hence has error at most . Therefore . Together with step 3.1, equality holds and the infimum is attained.
A nonzero best polynomial error must alternate at n+2 extrema
Statement
Let with , let , and let . If is a best approximant with , then there are ordered points in and such that
Facts & Assumptions
Given: with , , , and a best approximant with .
Best uniform polynomial error on a compact interval: is the attained uniform error on the compact interval .
A polynomial with the sign pattern of the active extrema: if the active extrema of a continuous residual have no alternating points, a polynomial of degree at most has the same strict sign as that residual on its full active-extrema set.
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function and For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: polynomial differences are continuous, and continuous real functions attain extrema on compact subsets of .
Proof
Put . It is continuous by [F3] and has norm by [F1]. Since is compact, [F3] makes attain , so its active-extrema set is nonempty. Suppose there are not ordered alternating points in . Applying [F2] gives a polynomial of degree at most with for every . In particular, is nonzero on .
The set is compact because it is a closed subset of . The continuous function therefore has a positive minimum on by [F3]. The set is a relatively open neighborhood of , by continuity and step 1.1.
If is nonempty, it is compact and contains no point of , so [F3] gives . If it is empty, set ; the outside bound is then vacuous.
The polynomial is continuous and has finite norm. Choose with . On , and have the same sign, , and , so . On , when nonempty, . Thus , while , contradicting that is best. Hence the required alternating extrema exist.
The best uniform polynomial approximant is unique
Statement
For every with and every , there is exactly one with .
Facts & Assumptions
Given: with , , and .
Best uniform polynomial error on a compact interval: is the infimum of the uniform errors over .
Best uniform polynomial approximants exist: at least one polynomial in attains .
A nonzero best polynomial error must alternate at n+2 extrema: if the best error is positive, a best polynomial has ordered alternating extremal points.
Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials: every real polynomial of degree at most has a coefficient-list representative.
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero real polynomial of degree at most has at most distinct real roots.
Proof
Let both attain . If , then , so at every point of .
Suppose and set . The triangle inequality gives , while [F1] gives ; hence is also best. By [F3], choose ordered alternating extrema for .
At each , equals either or . It is the average of and , each lying in . An average of two numbers in this interval equals only if both are , and equals only if both are . Thus for all nodes.
Choose coefficient-list representatives of using [F4]. The polynomial has degree at most and vanishes at the distinct points from step 2.1. If it were nonzero, [F5] would bound its distinct real roots by , a contradiction. Thus , so their restrictions satisfy . Existence is [F2], hence the best approximant is unique.
A budgeted polynomial Remez exchange terminates within its finite cap
Statement
For any finite integer budget , the procedure Finite-budget polynomial Remez exchange with an exact extrema oracle makes at most linear solves and at most oracle calls (at most calls total) and returns a status, provided each exact linear solve and each oracle call terminates. This is a finite-budget termination statement, not an iterative convergence theorem.
Facts & Assumptions
Given: The algorithm inputs and exact-solver/oracle behavior of Finite-budget polynomial Remez exchange with an exact extrema oracle, with a finite integer budget and terminating exact solves and oracle calls.
Finite-budget polynomial Remez exchange with an exact extrema oracle: each loop iteration makes one exact alternation solve and one extrema-oracle call, then either returns a status or updates one reference node.
The Remez alternation linear system has a unique solution: each alternation solve has a unique solution for the supplied distinct ordered reference set.
Proof
At the start of each iteration the reference is ordered and distinct: it is so initially, and each permitted replacement preserves strict order. For an interior index the new node lies strictly between its neighboring reference nodes; at index it satisfies , and at index it satisfies , so endpoint equality still leaves all reference nodes distinct and ordered. Therefore [F2] supplies a unique solve on every entered iteration, and the termination hypothesis together with [F1] gives a terminating oracle call or one reference update.
The loop indices are exactly , a finite set of iterations. Each entered iteration makes one linear solve and at most one oracle call, so there are at most solves and oracle calls in total. Each iteration returns or advances once, so after at most iterations the procedure either has returned earlier or returns budget-exhausted.
Weighted rational minimizers satisfy the defect-sensitive alternation criterion
Statement
Let with , let , and let with on . A best rational approximant in exists and is unique. For a candidate with positive weighted error , let . Then is best if and only if there are ordered nodes in and such that If the best error is , positivity of forces the unique best function to be itself. In the normal case the count is ; for defective candidates the count is .
Facts & Assumptions
Given: A nondegenerate compact interval , integers , continuous real functions on with , and a candidate .
Weighted rational minimax families and defect: the rational family has positive reduced denominators, weighted error , defect , effective perturbation degree , and the zero-function convention .
Best weighted rational approximants exist: a candidate attaining exists.
The rational first-variation space has dimension set by defect: for a candidate of defect , the polynomials with and form exactly .
A polynomial with the sign pattern of the active extrema: if a continuous residual has no alternating active extrema, a polynomial of degree at most can be chosen with the same strict sign on every active extremum.
Alternating interpolation weights annihilate lower-degree polynomials: for ordered nodes, set . Every polynomial of degree at most has , and if for one fixed then all node values are zero.
Alternating polynomial signs force the Haar zero-count contradiction: a nonzero polynomial of degree at most cannot have nonzero alternating signs at ordered nodes.
Reduced real rational representations are unique up to scale: a rational function has a unique coprime reduced pair up to scale, and a positive coefficient-norm normalization fixes that scalar.
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function: sums, products, and quotients by nowhere-zero continuous functions are continuous.
Intermediate value theorem, by bisection with a canonical left-half rule: a continuous function on takes every value between and : a continuous real function on an interval takes every value between its endpoint values.
For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: continuous real functions on compact intervals attain extrema, and closed subsets of compact intervals are compact.
Proof
First suppose and that has alternating weighted extrema. If another admissible had strictly smaller weighted error, write and , so . Then . Since , the numerator has nonzero alternating signs at those nodes. It lies in by [F3], contradicting [F6]. Thus no rational candidate has smaller weighted error and is best.
Now suppose is best with , and put . It is continuous by [F8], and [F10] makes attain on compact , so its active-extrema set is nonempty and compact. If it had no alternating points, [F4] would give a polynomial with on that set. By [F3], choose with , , and .
If the best error is zero, then everywhere; since , pointwise, so the best rational function is unique.
Since on compact , [F10] gives . For all sufficiently small , on . Reduce by [F7]. Its reduced denominator has no zero on , since it divides ; it is continuous by [F8] and has constant sign by [F9], so a common sign change makes it positive and a further positive scaling gives coefficient norm . Thus , as a rational function, is represented by an admissible member of . Its weighted residual is , where . On the active-extrema set, has the same sign as and is bounded away from zero. By [F8] and [F10], and are continuous and bounded; the denominator bound implies uniformly as . Hence there are a neighborhood of that set and a constant such that , and have the same sign, and on for all sufficiently small . Shrink further so ; then on and . The compact complement , if nonempty, contains no active extremum, so [F10] gives there for some ; a further small choice of makes . Hence , contradicting best approximation. This proves necessity.
Suppose the best error is positive and are both best. By step 2.1, has alternating nodes. At each such node, has weak sign , because . The numerator lies in by [F3], and its values have the same weak alternating signs since . Thus with , so [F5] gives and hence .
Steps 1.1 and 2.1 prove the two directions of the criterion for positive error; steps 1.3 and 3.1 prove uniqueness in the zero- and positive-error cases. Existence is [F2].
Finite-budget weighted rational Remez exchange with defect status
Definition
Inputs. A nondegenerate interval , degrees , continuous real target , strictly positive continuous weight , a finite exchange integer budget , an initial reference satisfying , an exact rational alternation-solver procedure, and an exact weighted-extrema oracle.
The alternation solver is an explicit input procedure. On a reference , it solves the nonlinear equations with coprime, , , on , and . It reports one of: a unique candidate with defect and its solved level ; an exact defective candidate with its computed and solved level ; singular or nonunique system; no normal solution; or inconclusive. The procedure does not assume that this nonlinear solve always succeeds.
The weighted-extrema oracle for a returned candidate reports one of:
- alternation only when and consists of ordered distinct points in with weighted residuals ;
- maximum only when , and ; or
- inconclusive.
Procedure. For , call the alternation solver on the current reference. Return its singular, no-normal-solution, or inconclusive status unchanged. If it returns a defective candidate, return defective immediately; do not apply the normal -point count. For a normal candidate , query the weighted-extrema oracle. If it returns inconclusive, return oracle-inconclusive. A response alternation or maximum returns exact, since either certifies zero weighted error. An alternation response with returns certified, with optimality supplied by Weighted rational minimizers satisfy the defect-sensitive alternation criterion. For maximum with , the current reference itself is an exact global alternation certificate, so return certified. If , return zero-level-no-exchange. If , return budget-exhausted before attempting an exchange. Otherwise ; exchange the returned maximum point for the smallest reference node whose expected sign matches the sign of and for which replacing by preserves strict ordering. If no such index exists, return no-valid-exchange. After an exchange, continue to the next iteration.
Outputs. The procedure returns an exact minimax certificate, an exact zero-error candidate, or an explicit defect, singularity, no-solution, oracle, exchange, or budget status. It claims no existence of a successful nonlinear solve and no iterative convergence.
A budgeted weighted rational Remez exchange terminates within its finite cap
Statement
For a finite integer budget , the procedure Finite-budget weighted rational Remez exchange with defect status makes at most alternation solver calls and at most extrema-oracle calls (at most calls total) and returns an explicit status, provided each input procedure terminates. This does not assert that the nonlinear solver succeeds or that the exchange iterates converge.
Facts & Assumptions
Given: The input and status contract of Finite-budget weighted rational Remez exchange with defect status, with a finite integer budget and terminating solver/oracle calls.
Finite-budget weighted rational Remez exchange with defect status: each iteration makes one solver call and, only after a normal candidate, at most one extrema-oracle call; it either returns a status or exchanges one reference node.
Proof
Every entered iteration has exactly one solver call; if the solver returns a status, the procedure terminates. If it returns a defective candidate, the explicit defect branch terminates. Otherwise a normal candidate receives one oracle call. An inconclusive response returns oracle-inconclusive; every other response either returns a certificate or zero-level status, or searches the finite reference set for an exchange and then returns no-valid-exchange or updates one node. Thus each iteration terminates under the given termination hypothesis.
The loop has only the indices , so it has at most iterations. Each iteration makes one solver call and at most one oracle call, giving at most calls to each procedure. If no earlier branch returns, the final iteration is followed by budget-exhausted. Therefore the procedure terminates within the stated total call bound.
5 · Examples, counterexamples and false statements
None yet.
Sources
- Lloyd N. Trefethen, Approximation Theory and Approximation Practice, Chapter 10
- András Kroó and Allan Pinkus, Strong Uniqueness, §§4.2–4.4
- András Kroó and Allan Pinkus, Strong Uniqueness, §4.4
- Tobin A. Driscoll and Richard J. Braun, Fundamentals of Numerical Computation, Polynomial interpolation
- Lloyd N. Trefethen, Approximation Theory and Approximation Practice, Chapter 5
- András Kroó and Allan Pinkus, Strong Uniqueness, §6.5
- Lloyd N. Trefethen, Approximation Theory and Approximation Practice, Chapter 24
- András Kroó and Allan Pinkus, Strong Uniqueness, §6.2
- András Kroó and Allan Pinkus, Strong Uniqueness, §6
- Ricardo Pachón and Lloyd N. Trefethen, Barycentric-Remez Algorithms for Best Polynomial Approximation in the Chebfun System, §2.1
- András Kroó and Allan Pinkus, Strong Uniqueness, §4
- Ricardo Pachón and Lloyd N. Trefethen, Barycentric-Remez Algorithms for Best Polynomial Approximation in the Chebfun System, §§2.1–2.2
- András Kroó and Allan Pinkus, Strong Uniqueness, Lemma 6.2
- András Kroó and Allan Pinkus, Strong Uniqueness, §6.1
- Ricardo Pachón and Lloyd N. Trefethen, Barycentric-Remez Algorithms for Best Polynomial Approximation in the Chebfun System, §2
- András Kroó and Allan Pinkus, Strong Uniqueness, §§6.2–6.5
- Silviu-Ioan Filip, Yuji Nakatsukasa, Lloyd N. Trefethen, and Bernhard Beckermann, Rational Minimax Approximation via Adaptive Barycentric Representations, §§3–4.1
- Silviu-Ioan Filip, Yuji Nakatsukasa, Lloyd N. Trefethen, and Bernhard Beckermann, Rational Minimax Approximation via Adaptive Barycentric Representations, §§3–4