Tarski's Exponential Function Problemkrapp/research/2017_07_06_slides.pdf · Tarski’s...

Introduction Tarski’s Decision Algorithm Decidability of the Real Exponential Field

Tarski’s Exponential Function Problem

Lothar Sebastian Krapp

Universität Konstanz, Fachbereich Mathematik und Statistik

06 July 2017

Logic Colloquium

Lothar Sebastian Krapp Tarski’s Exponential Function Problem

1 Introduction

2 Tarski’s Decision Algorithm

3 Decidability of the Real Exponential Field

1 Introduction

Statement of the Problem

QuestionIs (R,+,−, ·, 0, 1, <, exp) decidable? (Tarski 1948)

— or equivalently —

QuestionIs the theory of (R,+,−, ·, 0, 1, <, exp) recursively axiomatizable?

Setting: Model Theory

• fixed logical framework (logical connectives, quantifiers, notion of provability, etc.)• specific model theoretical language consisting of constants, unary functions, andrelations(e.g. Lor = {+,−, ·, 0, 1, <})

• formulas in the language(e.g. ∀x (x < 0→ ∃y y · y = x))

• structures interpreting the language(e.g. (R,+,−, ·, 0, 1, <))

• fixed logical framework (logical connectives, quantifiers, notion of provability, etc.)

• specific model theoretical language consisting of constants, unary functions, andrelations(e.g. Lor = {+,−, ·, 0, 1, <})

Notation

Fix a language L and let φ be an L-sentence andM an L-structure.

T — an L-theory, i.e. a set of L-sentences (L-formulas without free variables)T |− φ — φ is provable from T

(e.g. {∀x ¬x · x < 0} |− ¬1 · 1 < 0)

M |= φ — φ is valid inM(e.g. (R,+,−, ·, 0, 1, <) |= ∃x x · x = 1 + 1,but (Q,+,−, ·, 0, 1, <) 6|= ∃x x · x = 1 + 1)

T |= φ — for allM |= T we haveM |= φ

Th(M) = {ψ| ψ is an L-sentence andM |= ψ} — the complete theory ofM

Notation

T — an L-theory, i.e. a set of L-sentences (L-formulas without free variables)

T |− φ — φ is provable from T(e.g. {∀x ¬x · x < 0} |− ¬1 · 1 < 0)

Notation

(e.g. {∀x ¬x · x < 0} |− ¬1 · 1 < 0)

Notation

(e.g. {∀x ¬x · x < 0} |− ¬1 · 1 < 0)

Notation

(e.g. {∀x ¬x · x < 0} |− ¬1 · 1 < 0)

Notation

(e.g. {∀x ¬x · x < 0} |− ¬1 · 1 < 0)

Decidability

DefinitionAn L-structureM is called decidable if there exists an algorithm that determineswhether for a given L-sentence φ we haveM |= φ orM |= ¬φ.

DefinitionA complete L-theory T is called recursive if there exists an algorithm that determineswhether for a given L-sentence φ we have T |= φ or T |= ¬φ.

DefinitionAn L-theory T is axiomatized by an L-theory Σ if for all L-sentences φ it holds T |= φif and only if Σ |= φ.

Decidability

TheoremAn L-structureM is decidable if and only if Th(M) is recursively axiomatizable.

Sketch of proof.⇒ IfM is decidable, then Th(M) is recursive.⇐ Let Σ be a recursive axiomatization of Th(M) and let {φ0, φ1, . . .} be a recursive

enumeration of all sentences provable from Σ.Given a sentence ψ, check in turn whether ψ or ¬ψ is the nth entry of the list andterminate when the entry is found. If ψ = φn, then Σ |− ψ, and hence (by Gödel’sCompleteness Theorem) Σ |= ψ. Thus, Th(M) |= ψ, i.e.M |= ψ.Similarly, if ψ = ¬φn, thenM |= ¬φn.

Decidability

Sketch of proof.⇒ IfM is decidable, then Th(M) is recursive.

⇐ Let Σ be a recursive axiomatization of Th(M) and let {φ0, φ1, . . .} be a recursiveenumeration of all sentences provable from Σ.Given a sentence ψ, check in turn whether ψ or ¬ψ is the nth entry of the list andterminate when the entry is found. If ψ = φn, then Σ |− ψ, and hence (by Gödel’sCompleteness Theorem) Σ |= ψ. Thus, Th(M) |= ψ, i.e.M |= ψ.Similarly, if ψ = ¬φn, thenM |= ¬φn.

Decidability

enumeration of all sentences provable from Σ.

Given a sentence ψ, check in turn whether ψ or ¬ψ is the nth entry of the list andterminate when the entry is found. If ψ = φn, then Σ |− ψ, and hence (by Gödel’sCompleteness Theorem) Σ |= ψ. Thus, Th(M) |= ψ, i.e.M |= ψ.Similarly, if ψ = ¬φn, thenM |= ¬φn.

Decidability

enumeration of all sentences provable from Σ.Given a sentence ψ, check in turn whether ψ or ¬ψ is the nth entry of the list andterminate when the entry is found. If ψ = φn, then Σ |− ψ, and hence (by Gödel’sCompleteness Theorem) Σ |= ψ. Thus, Th(M) |= ψ, i.e.M |= ψ.

Similarly, if ψ = ¬φn, thenM |= ¬φn.

Decidability

enumeration of all sentences provable from Σ.Given a sentence ψ, check in turn whether ψ or ¬ψ is the nth entry of the list andterminate when the entry is found. If ψ = φn, then Σ |− ψ, and hence (by Gödel’sCompleteness Theorem) Σ |= ψ. Thus, Th(M) |= ψ, i.e.M |= ψ.Similarly, if ψ = ¬φn, thenM |= ¬φn.

1 Introduction

The Theorem

Fix language of ordered rings Lor = {+,−, ·, 0, 1, <} and let R = (R,+,−, ·, 0, 1, <).

Theorem (Tarski 1948)The Lor-structure R is decidable.

Tarski provides an “inner-mathematical” decision algorithm.

The Theorem

Outline of Tarski’s Decision AlgorithmFirst step: Construction of a quantifier elimination algorithm

• For each formula φ(x1, . . . , xn) (with free variables from x1, . . . , xn) we find aquantifier-free formula ψ(x1, . . . , xn) such that

R |= ∀x1 . . . ∀xn (φ(x1, . . . , xn)↔ ψ(x1, . . . , xn)).

(E.g. ∃y x · x = y is equivalent to (−32x < 0 ∨ −32x = 0))• Quantifier elimination algorithm by structural induction: If φ and φ′ are equivalentto quantifier-free formulas ψ and ψ′, respectively, then

- ¬φ is equivalent to ¬ψ.- φ ∨ φ′ is equivalent to ψ ∨ ψ′.- φ ∧ φ′ is equivalent to ψ ∧ ψ′.- ∃x φ is equivalent to ψ if x does not appear free in ψ.

R |= ∀x1 . . . ∀xn (φ(x1, . . . , xn)↔ ψ(x1, . . . , xn)).

(E.g. ∃y x · x = y is equivalent to (−32x < 0 ∨ −32x = 0))

• Quantifier elimination algorithm by structural induction: If φ and φ′ are equivalentto quantifier-free formulas ψ and ψ′, respectively, then

R |= ∀x1 . . . ∀xn (φ(x1, . . . , xn)↔ ψ(x1, . . . , xn)).

(E.g. ∃y x · x = y is equivalent to (−32x < 0 ∨ −32x = 0))• Quantifier elimination algorithm by structural induction: If φ and φ′ are equivalentto quantifier-free formulas ψ and ψ′, respectively, then

Outline of Tarski’s Decision AlgorithmFirst step (continued)

• Special attention to the case ∃x φ, where x appears free in ψ. Now, ∃x φ isequivalent to ∃x ψ, where ψ is quantifier-free.

• Quantifier-free formulas in the variables x , y1, . . . , ym are equivalent to a formulaof the form

n∧i=1

ki∨j=1

ψij(x , y1, . . . , ym),

where each ψij(x , y1, . . . , ym) is of the form

pij(x , y1, . . . , ym) = 0 or pij(x , y1, . . . , ym) > 0

for some polynomials pij ∈ Z[x , y1, . . . , ym]. (E.g. x − y1 = y1 + y2 ∧ ¬y1 < y2 isequivalent to x − 2y1 − y2 = 0 ∧ (y1 − y2 > 0 ∨ y1 − y2 = 0).)

n∧i=1

ki∨j=1

ψij(x , y1, . . . , ym),

n∧i=1

ki∨j=1

ψij(x , y1, . . . , ym),

• Main technical step: For each formula of the form

∃x pij(x , y1, . . . , ym) = 0 or ∃x pij(x , y1, . . . , ym) > 0

we can use geometrical arguments to obtain an equivalent quantifier-free formulaψ′

ij(x , y1, . . . , ym).(E.g. ∃x y1x2 + y2x + y3 = 0 if and only if the discriminant y 2

2 − 4y1y3 is non-negative or thepolynomial is linear or the zero polynomial, i. e.¬y 2

2 − 4y1y3 < 0 ∨ (y1 = 0 ∧ ¬ y2 = 0) ∨ (y1 = y2 = y3 = 0).)

• Main technical step: For each formula of the form

∃x pij(x , y1, . . . , ym) = 0 or ∃x pij(x , y1, . . . , ym) > 0

we can use geometrical arguments to obtain an equivalent quantifier-free formulaψ′

ij(x , y1, . . . , ym).(E.g. ∃x y1x2 + y2x + y3 = 0 if and only if the discriminant y 2

2 − 4y1y3 is non-negative or thepolynomial is linear or the zero polynomial, i. e.¬y 2

2 − 4y1y3 < 0 ∨ (y1 = 0 ∧ ¬ y2 = 0) ∨ (y1 = y2 = y3 = 0).)

Outline of Tarski’s Decision AlgorithmSecond step: Applying the Quantifier Elimination Algorithm

• By Step 1, each sentence ψ is equivalent to a quantifier-free sentence. Aquantifier-free sentence is equivalent to one of the form

n∧i=1

ki∨j=1

ψij ,

where each ψij is of the form zij = 0 or zij > 0 for some zij ∈ Z.(E.g. ∃y (1 + 1) · (1 + 1) = y is equivalent to (−64 < 0 ∨ −64 = 0).)

• Performing a numerical check gives us whether this sentence is valid in R or not.

n∧i=1

ki∨j=1

ψij ,

n∧i=1

ki∨j=1

ψij ,

• Tarksi’s Decision Algorithm only used the following properties of the real numbers:

1 R is a field2 it is linearly ordered3 all positive elements are squares4 polynomials of odd degree have zeros

Formalizing these statements yields the so calledtheory of real closed fields,which is usually denoted by RCF.

1 R is a field

2 it is linearly ordered3 all positive elements are squares4 polynomials of odd degree have zeros

1 R is a field2 it is linearly ordered

3 all positive elements are squares4 polynomials of odd degree have zeros

1 R is a field2 it is linearly ordered3 all positive elements are squares

4 polynomials of odd degree have zeros

• Tarksi’s Decision Algorithm only used the following properties of the real numbers:• the field axioms (commutativity of + and ·, distributivity, existence of inverses etc.)• the order axioms:

• ∀x ¬x < x• ∀x∀y∀z ((x < y ∧ y < z)→ x < z)• ∀x∀y (x < y ∨ x = y ∨ y < x)• ∀x∀y∀z (x < y → x + z < y + z)• ∀x∀y ((0 < x ∧ 0 < y)→ 0 < x · y)

• positive elements are squares:• ∀x (0 < x → ∃y x = y 2)

• polynomials of odd degree have zeros: For each n ∈ N we have:• ∀x0∀x1 . . .∀x2n ∃y y 2n+1 + x2ny 2n + x2n−1y 2n−1 + . . .+ x1y + x0 = 0.

The Theory of Real Closed FieldsImportant Consequences

• Each model of RCF has effective quantifier elimination and is decidable.• RCF is complete, i.e. for each Lor-sentence φ we have RCF |= φ or RCF |= ¬φ.• As RCF is a complete recursive theory, we obtain a second, recursion theoreticdecision algorithm for R. (This would not be the case if properties of R had beenused which cannot be formalised as axioms.)

• Each model of RCF has effective quantifier elimination and is decidable.

• RCF is complete, i.e. for each Lor-sentence φ we have RCF |= φ or RCF |= ¬φ.• As RCF is a complete recursive theory, we obtain a second, recursion theoreticdecision algorithm for R. (This would not be the case if properties of R had beenused which cannot be formalised as axioms.)

• Each model of RCF has effective quantifier elimination and is decidable.• RCF is complete, i.e. for each Lor-sentence φ we have RCF |= φ or RCF |= ¬φ.

• As RCF is a complete recursive theory, we obtain a second, recursion theoreticdecision algorithm for R. (This would not be the case if properties of R had beenused which cannot be formalised as axioms.)

• Each model of RCF has effective quantifier elimination and is decidable.• RCF is complete, i.e. for each Lor-sentence φ we have RCF |= φ or RCF |= ¬φ.• As RCF is a complete recursive theory, we obtain a second, recursion theoreticdecision algorithm for R.

(This would not be the case if properties of R had beenused which cannot be formalised as axioms.)

• Each model of RCF has effective quantifier elimination and is decidable.• RCF is complete, i.e. for each Lor-sentence φ we have RCF |= φ or RCF |= ¬φ.• As RCF is a complete recursive theory, we obtain a second, recursion theoreticdecision algorithm for R. (This would not be the case if properties of R had beenused which cannot be formalised as axioms.)

1 Introduction

Decidability of the Real Exponential Field

Denote by Lexp = {+,−, ·, 0, 1, <, exp} the language of exponential rings and letRexp = (R,+,−, ·, 0, 1, <, exp) be the real exponential field.

QuestionTarski (1948): Is Rexp decidable?

Decidability of the Real Exponential Field

Denote by Lexp = {+,−, ·, 0, 1, <, exp} the language of exponential rings and letRexp = (R,+,−, ·, 0, 1, <, exp) be the real exponential field.

QuestionTarski (1948): Is Rexp decidable?

Progress

• 1980s (Dahn and Wolter): Several candidates for recursive axiomatizations of Rexp

• 1996 (Wilkie): Rexp is model complete: That means that for every Lexp-formulaφ(x1, . . . , xn) there exists an equivalent Lexp-formula of the form∃y1 . . . ∃ym ψ(x1, . . . , xn, y1, . . . , ym), where ψ is quantifier-free.

• 1996 (Macintyre and Wilkie): Assuming Real Schanuel’s Conjecture, Rexp isdecidable.

• 1998 (van den Dries): Rexp does not admit quantifier elimination.• 2006 (Berarducci, Servi): Assuming Transfer Conjecture, Rexp is decidable.

Progress

• 1996 (Wilkie): Rexp is model complete

: That means that for every Lexp-formulaφ(x1, . . . , xn) there exists an equivalent Lexp-formula of the form∃y1 . . . ∃ym ψ(x1, . . . , xn, y1, . . . , ym), where ψ is quantifier-free.

Progress

• 1998 (van den Dries): Rexp does not admit quantifier elimination.

• 2006 (Berarducci, Servi): Assuming Transfer Conjecture, Rexp is decidable.

Progress

Schanuel’s Conjecture

Schanuel’s Conjecture (SC)Let α1, . . . , αn ∈ C be linearly independent over Q. Then

tdQ(Q(α1, . . . , αn, eα1 , . . . , eαn )) ≥ n.

→ (SC) relates the algebraic complexity of complex numbers with the algebraiccomplexity of their exponentials.

Example: (SC) implies that e and π are algebraically independent over Q. This means that expressionslike 1

26 e3 + eπ − π4

2 + e2π2

2 + e (≈ −0, 2104...) are never equal to 0.

tdQ(Q(α1, . . . , αn, eα1 , . . . , eαn )) ≥ n.

26 e3 + eπ − π4

2 + e2π2

tdQ(Q(α1, . . . , αn, eα1 , . . . , eαn )) ≥ n.

26 e3 + eπ − π4

2 + e2π2

Macintyre and Wilkie’s conditional proof

1 There exists a recursive Lexp-theory T0 ⊆ Th(Rexp) such that T0 ∪ T∃(Rexp)axiomatizes Th(Rexp).→ Problem reduces to finding a recursive axiomatization of Th∃(Rexp).

2 Each existential sentence in Th(Rexp) is equivalent to one of the form

∃z1 . . . ∃zn p(z1, . . . , zn, exp(z1), . . . , exp(zn)) = 0

for some p(x1, . . . , xn, y1, . . . , yn) ∈ Z[x1, . . . , xn, y1, . . . , yn].Example: ∃x∃y exp(exp(x)) > y + x is equivalent to∃x∃y∃z1∃z2 (z1 − exp(x) = 0 ∧ exp(z1)− y + x − z2

2 = 0).This, however, is equivalent to∃x∃y∃z1∃z2 ((z1 − exp(x))2 + (exp(z1)− y + x − z2

2 )2 = 0).→ Problem reduces to finding an algorithm which determines whether apolynomial p(x1, . . . , xn, y1, . . . , yn) ∈ Z[x1, . . . , xn, y1, . . . , yn] has a zero of theform (a1, . . . , an, exp(a1), . . . , exp(an)) or not.

3 Assuming (SC), there exists a recursive theory T1 axiomatizing Th∃(Rexp).

Macintyre and Wilkie’s conditional proof1 There exists a recursive Lexp-theory T0 ⊆ Th(Rexp) such that T0 ∪ T∃(Rexp)

axiomatizes Th(Rexp).

→ Problem reduces to finding a recursive axiomatization of Th∃(Rexp).2 Each existential sentence in Th(Rexp) is equivalent to one of the form

axiomatizes Th(Rexp).→ Problem reduces to finding a recursive axiomatization of Th∃(Rexp).

for some p(x1, . . . , xn, y1, . . . , yn) ∈ Z[x1, . . . , xn, y1, . . . , yn].

Example: ∃x∃y exp(exp(x)) > y + x is equivalent to∃x∃y∃z1∃z2 (z1 − exp(x) = 0 ∧ exp(z1)− y + x − z2

for some p(x1, . . . , xn, y1, . . . , yn) ∈ Z[x1, . . . , xn, y1, . . . , yn].Example: ∃x∃y exp(exp(x)) > y + x

is equivalent to∃x∃y∃z1∃z2 (z1 − exp(x) = 0 ∧ exp(z1)− y + x − z2

2 = 0).

This, however, is equivalent to∃x∃y∃z1∃z2 ((z1 − exp(x))2 + (exp(z1)− y + x − z2

2 )2 = 0).

→ Problem reduces to finding an algorithm which determines whether apolynomial p(x1, . . . , xn, y1, . . . , yn) ∈ Z[x1, . . . , xn, y1, . . . , yn] has a zero of theform (a1, . . . , an, exp(a1), . . . , exp(an)) or not.

3 Assuming (SC), there exists a recursive theory T1 axiomatizing Th∃(Rexp).Lothar Sebastian Krapp Tarski’s Exponential Function Problem

Consequences

As (SC) is “probably true”, the proposed recursive theory T0 ∪ T1 “probably” gives us adecision algorithm for Rexp.

BUT: Its computational complexity makes it useless for applications in the real world.

Consequences

As (SC) is “probably true”, the proposed recursive theory T0 ∪ T1 “probably” gives us adecision algorithm for Rexp.

BUT: Its computational complexity makes it useless for applications in the real world.

References

[1] A. Berarducci and T. Servi, ‘An effective version of Wilkie’s theorem of the complementand some effective o-minimality results’, Ann. Pure Appl. Logic 125 (2004) 43–74.

[2] A. Macintyre and A. Wilkie, ‘On the decidability of the real exponential field’,Kreiseliana: about and around Georg Kreisel (ed. P. Odifreddi; A. K. Peters, Wellesley, MA,1996) 441–467.

[3] A. Tarski, A decision method for elementary algebra and geometry (RAND Corporation,Santa Monica, CA, 1948).

[4] A. Wilkie, ‘Model completeness results for expansions of the ordered Field of real numbersby restricted Pfaffian functions and the exponential function’, J. Amer. Math. Soc. 9 (1996)1051–1094.

Tarski's Exponential Function Problemkrapp/research/2017_07_06_slides.pdf · Tarski’s...

Documents

Nut Exponential

Exponential Functions and Their Graphs. 2 The exponential function f with base b is defined by f(x) = ab x where b > 0, b 1, and x is any real number

Negative Exponents Fraction Exponent Graphs Exponential function Misc. 10 20 30 40 50

Warm up Graph. Lesson 11-3 The Number e Objective: To use the exponential function y = e x

Exponential Functions 2.pdf

The Power of Tarski’s Relation Algebra on Trees1/35 The Power of Tarski’s Relation Algebra on Trees Jelle Hellings1 Yuqing Wu2 Marc Gyssens1 Dirk Van Gucht3 1 Hasselt University

Conjugate Priors, Uninformative Priors · The Exponential Family A probability mass function (pmf) or probability distribution function (pdf) p(Xj ), for X= (X 1;:::;X m) 2Xm and

ฟังก์ชันเอกซ์โพเนนเชียล (Exponential Function) · ข้อตกลง ในหนังสือคณิตศาสตร์บางเล่มให้ข้อกําหนดของฟ

โจทย์ exponential เริ่มต้น

Econopolis Exponential Technologies Fund

Construction and Application of Modifying Weights with ... · PDF fileSchematic diagram and flowchart of FOA is shown in ... represents exponential function and δ is kernel function

HOLT-WINTER EXPONENTIAL SMOOTHING … EXPONENTIAL SMOOTHING •Pada dasarnya terdapat dua tipe metode Holt-Winter exponential: Additive Multiplicative •Parameter yang digunakan adalah

Kompetenzmaterialien–Exponential-undLogarithmusfunktionen · 2020. 11. 5. · MathematikmachtFreu(n)de KH–Exponential-undLogarithmusfunktionen KOMPETENZHEFT – EXPONENTIAL- UND

09 exponential

Complex Roots - University of Notre Dametaylor/Math20580/Lectures/Lb3.3/... · 2019-04-24 · The complex exponential function Suppose z= + i is a complex number with and real numbers

Exponential Poison

EXPLORE MORE. 311 INSTITUTE. · THE EXPONENTIAL FUTURE 311 Institute GLOBAL ADVISORY : FORECASTING : INNOVATION : STRATEGY UNLIMITED THINKING . EXPONENTIAL POTENTIAL. EXPONENTIAL

SIMPLE EVALUATION METHODS FOR ROAD Title ...repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/...roughness itself, and also the prediction model using exponential function proposed

Exponential and logarithm function

Exponential modeling