From Linear Logic to Differential Linear Logictranquil/content/docs/aila2008.pdfLinear? Differential...

Linear? Differential LL does it better Proof Nets and Lambda-Calculus Differential Nets and Resource Calculus

From Linear Logic toDifferential Linear Logic

Paolo Tranquilli

Dipartimento di MatematicaUniversità degli Studi Roma Tre

Preuves, Programmes et SystèmesUniversité Denis-Diderot Paris 7

AILA – XXIII Incontro di Logica – 20/02/2008

Outline

1 Linear? Differential LL does it betterWhy Linear Logic is linear?Differential Linear Logic

2 Proof Nets and Lambda-CalculusA running exampleThe translation

3 Differential Nets and Resource CalculusLinearization of boxesResource Calculus and its translation

Outline

Why Linear Logic is linear?

Vector spaces and LL: is there a link?

Following Pagani and Maieli’s talk, you may have noted anextensive use of jargon borrowed from vector spaces andanalysis:

Linear, dual (A⊥), exponential, tensor (⊗), direct sum (⊕)

What is the catch?

Following Maieli’s talk, you may have noted an extensiveuse of jargon borrowed from vector spaces and analysis:

What is the catch?

Following Maieli’s talk and hopefully Pagani’s one thisFriday, you may note an extensive use of jargon borrowedfrom vector spaces and analysis:

What is the catch?

The link is there, though somewhat vague

The ground for LL (Girard, 1987) was the study by Girardof System F (in fact, second order intuitionistic sequentcalculus or natural deduction)

Girard imagined formulas A as spaces (coherent spaces)given by atomic bits of information |A|

Moreover, he imagined this bits to be a sort of basis for avector space

...“sort of”?

The link is there, though somewhat vague

The ground for LL (Girard, 1987) was the study by Girardof System F (in fact, second order intuitionistic sequentcalculus or natural deduction)

Girard imagined formulas A as spaces (coherent spaces)given by atomic bits of information |A|

Moreover, he imagined this bits to be a sort of basis for avector space

...“sort of”?

Linearity in Linear Logic

Factorization of the intuitionistic arrow

A⇒ B = !A⊸ B

More on the exponential modality later, now let us considerthe linear arrowLinearity in R-modules. . . when R = {0,1} (practically,sets):

F (U + V ) = F (U) + F (V )

F (aU) = aF (U)

A linear map F is completely determined by singletons (ifalso continous wrt directed union)In fact for a linear continous function F : A⊸ B atomic bitsin the input A contribute each exactly once to the output B

A⇒ B = !A⊸ B

F (U ∪ V ) = F (U) ∪ F (V )

F (∅) = ∅

A⇒ B = !A⊸ B

F (U ∪ V ) = F (U) ∪ F (V )

F (∅) = ∅

A⇒ B = !A⊸ B

F (U ∪ V ) = F (U) ∪ F (V )

F (∅) = ∅

A linear map F is completely determined by singletons (ifalso continous wrt directed union)In fact for a linear proof π : A⊸ B atomic bits in thehypothesis A contribute each exactly once to the thesis B

Linearity at last

in Logic

using a hypothesisexacly once

in Computer Science

using an input/resourceexacly once

in Analysis

F (aU + bV ) =aF (U) + bF (V ) (andcontinuity)

...though the link with the third is not really convincing for now...

Linearity at last

in Logic

using a hypothesisexacly once

in Computer Science

using an input/resourceexacly once

in Analysis

F (aU + bV ) =aF (U) + bF (V ) (andcontinuity)

...though the link with the third is not really convincing for now...

Differential Linear Logic

A note about duals

A⊥ in R-modules is the space of linear functions A⊸ R

This models negation: in case of bidual spaces (A ¾ A⊥⊥),there is an isomorphism between A⊸ B and B⊥ ⊸ A⊥

Moreover, at the core of the cut rule is the interactionbetween A and A⊥

U ∈ A,V ∈ A⊥ : 〈U,V 〉 =∑

x∈|A|

The condition defining coherent spaces is that the supportof 〈U,V 〉 is at most 1, i.e. #U ∩ V ≤ 1

Let us do it better than LL

We relax the condition and just ask 〈U,V 〉 to be finitesums:

#U ∩ V < ω

(finiteness spaces, Ehrhard 2003)

R 〈A〉, a submodule of R |A| (with a condition on supports),for any unitary semiring R yields spaces for which the allthe connectives really correspond to the vector spaceconstructs

For example: R 〈A⊸ B〉 is isomorphic to R 〈A〉⊸ R 〈B〉,i.e. the linear morphisms continuous in the lineartopologies of R 〈A〉 and R 〈B〉 (defined independently of thestructure of finiteness spaces)

Formal sums – 1

Condition #U ∩ V ≤ 1 in coherent spaces can be seen aspreventing proofs to be spread in sets of proofs duringreduction

Here the support of an interaction does not need to be asingleton, i.e. the result of an interaction is a generic(though finite) sum with coefficients in R: a proof canreduce to a sum!

What does it mean?

Formal sums – 2

The easiest way to regard this is non-deterministic choice.The sum keeps track of the various possible choices(exspecially if R =N):

σ55kkkkkk

))SSSSSS

{ σ→ π1 + π2

Other interpretations are possible, for example by takingR = R+ and interpreting probabilistic choice:

p 55kkkkkk

1−p))SSSSSS

{ σ→ pπ1 + (1 − p)π2

The exponential

A linear world would be boring. . . the exponential modality!A brings back contraction and weakening of hypotheses

In finiteness spaces, the modality operates a shift fromlinear formal sums to formal power series.

A⊸ B are linear functions

In fact a power xn is a multilinear use of multiple (possiblynone) occurrences of x

The exponential

A linear world would be boring. . . the exponential modality!A brings back duplication and erasing of inputs

The exponential

!A⊸ B are analytical functions

Linearity revisited

in Logic

Linear hypotheses, tobe used exacly once

in Computer Science

Linear inputs, to beused exactly once

in Analysis

Linear argumentsoccurring in linearposition

We will see some of the consequences that this approachbrings in the top areas

Linearity revisited

in Logic

Non-linear hypotheses,that can be contractedor weakened

in Computer Science

Non-linear inputs, thatcan be duplicated orerased

in Analysis

Non-linear argumentsoccurring as powers

Linearity revisited

in Logic

Proofs

in Computer Science

Programs

in Analysis

Analytical functions

Outline

A running example

Lambda-Calculus

A core functional programming language:

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

Linear Logic Proof-Nets provide a geometricalrepresentation of λ-terms

A running example

Lambda-Calculus

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

A running example

Lambda-Calculus

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

A running example

Lambda-Calculus

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

A running example

Lambda-Calculus

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

A running example

Lambda-Calculus

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

A running example

Lambda-Calculus

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

A running example

Lambda-Calculus

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

A running example

Lambda-Calculus

M ::= x | λx .M | (M)N

. . . with types:

x : A x : A,M : B =⇒ λx .M : A⇒ B,

M : A⇒ B,N : A =⇒ (M)N : B

A running example

An example

I = λy .y : α⇒ α

!α⊸ α

?(!α⊸ α)⊥

!(!α⊸ α)⊸ !α⊸ α

!α⊸ α

!(!α⊸ α)

A running example

An example

2 = λf , x .(f )(f )x : (α⇒ α)⇒ α⇒ α

!α⊸ α

?(!α⊸ α)⊥

!(!α⊸ α)⊸ !α⊸ α

!α⊸ α

!(!α⊸ α)

A running example

An example

(2)I = (λf , x .(f )(f )x)λy .y : α⇒ α

!α⊸ α

?(!α⊸ α)⊥

!(!α⊸ α)⊸ !α⊸ α

!α⊸ α

!(!α⊸ α)

A running example

An example

(2)I = (λf , x .(f )(f )x)λy .y

A running example

An example

(2)I = (λf , x .(f )(f )x)λy .y

A running example

An example

(2)I = (λf , x .(f )(f )x)λy .y

A running example

An example

(2)I = (λf , x .(f )(f )x)λy .y

A running example

An example

(2)I = (λf , x .(f )(f )x)λy .y

A running example

An example

(2)I = (λf , x .(f )(f )x)λy .y

A running example

An example

(2)Iβ→ λx .(λy .y)(λy .y)x

A running example

An example

A running example

An example

A running example

An example

(2)Iβ∗→ λx .x

The translation

The results

What we have is a translation M◦ from terms to proof nets.

Theorem (Girard, Danos-Regnier)

each redex (λx .S)T in M corresponds bijectively tomultiplicative cuts in M◦

Mβ→ N ⇐⇒ M◦

e∗։ N◦

Mβ∗→ N ⇐⇒ M◦

∗→ N◦

The translation

Inputs are packages

Inputs are packages that are

duplicable

erasable

openable

The translation

Inputs are packages

duplicable

erasable

openable

The translation

Inputs are packages

duplicable

erasable

openable

? !...

The translation

Inputs are packages

duplicable

erasable

openable

? !...

Outline

Linearization of boxes

Single-use hypotheses/inputs

There is a situation not covered by the properties of boxes

What if a hypothesis/input is available just once (afterwhich it “expires”), but is asked for more than once?

We can imagine that it can be assignednon-deterministically (formal sums involved)

We are able to see this in differential nets, the geometricalrepresentation of Differential Linear Logic (Ehrhard andRegnier, 2004)

Single-use hypotheses/inputs

There is a situation not covered by the properties of boxes

What if a hypothesis/input is available just once (afterwhich it “expires”), but is asked for more than once?

We can imagine that it can be assignednon-deterministically (formal sums involved)

We are able to see this in differential nets, the geometricalrepresentation of Differential Linear Logic (Ehrhard andRegnier, 2004)

Back to an example

!α⊸ α

!(!α⊸ α)

?(!α⊸ α)⊥

!(!α⊸ α)⊸ !α⊸ α

Back to an example

!α⊸ α

!(!α⊸ α)

?(!α⊸ α)⊥

!(!α⊸ α)⊸ !α⊸ α

Back to an example

Yet another example

Resource Calculus and its translation

Resource Calculus

Does this behaviour correspond to a calculus like Proof Nets dofor λ-calculus?Yes, λ-calculus with multiplicities (Boudol, 1993), exactlypresents this behaviour.

Terms of the resource λ-calculus

M ::= x | λx .M | 〈M〉A

A ::= Me11 · · ·M

ekk with ei ≤ ∞

Boudol described a non-deterministic reduction. We, as alreadyexplained, employ formal sums. The ground for this is regularsubstitution for M∞ arguments, and linear substitution forfinitely available arguments

Resource Calculus

M ::= x | λx .M | 〈M〉A

A ::= Me11 · · ·M

ekk with ei ≤ ∞

Resource Calculus

M ::= x | λx .M | 〈M〉A

A ::= Me11 · · ·M

ekk with ei ≤ ∞

Resource Calculus

M ::= x | λx .M | 〈M〉A

A ::= Me11 · · ·M

ekk with ei ≤ ∞

Resource Calculus

M ::= x | λx .M | 〈M〉A

A ::= Me11 · · ·M

ekk with ei ≤ ∞

Resource Calculus

M ::= x | λx .M | 〈M〉A

A ::= Me11 · · ·M

ekk with ei ≤ ∞

Resource Calculus

M ::= x | λx .M | 〈M〉A

A ::= Me11 · · ·M

ekk with ei ≤ ∞

Back to the examples

λf , x . 〈f 〉 (〈f 〉 x∞)∞⟩

λy .y {

λf , x . 〈f 〉 (〈f 〉 x∞)∞⟩

λy .y {

λf , x . 〈f 〉 (〈f 〉 x∞)∞⟩

λy .y↓ β

λx . 〈λy .y〉0∞ {

λf , x . 〈f 〉 (〈f 〉 x∞)∞⟩

λy .y↓ β

λx . 〈λy .y〉0∞ {

λf , x . 〈f 〉 (〈f 〉 x∞)∞⟩

(λy .y)2{

λf , x . 〈f 〉 (〈f 〉 x∞)∞⟩

(λy .y)2{

λf , x . 〈f 〉 (〈f 〉 x∞)∞⟩

(λy .y)2

↓ β2 · λx . 〈λy .y〉0∞ · 〈λy .y〉 x∞ { 2·

λf , x . 〈f 〉 (〈f 〉 x∞)∞⟩

(λy .y)2

↓ β2 · λx . 〈λy .y〉0∞ · 〈λy .y〉 x∞ { 2·

The results

What we have is a translation M◦ from terms to proof nets.

Theorem (Girard, Danos-Regnier)

each redex (λx .S)T in M corresponds bijectively tomultiplicative cuts in M◦

Mβ→ N ⇐⇒ M◦

e∗։ N◦

Mβ∗→ N ⇐⇒ M◦

∗→ N◦

The results

What we have is a translation M◦ from resource terms todifferential nets.

Theorem

each redex 〈λx .S〉A in M corresponds bijectively tomultiplicative cuts in M◦

Mβ→ N ⇐⇒ M◦

e∗։ N◦

Mβ∗→ N ⇐⇒ M◦

∗→ N◦

Conclusion

Concluding remarks

Linear substitution is differentiation:

∂x2y∂x· a =

∂x · x · y∂x

· a = a · x · y + x · a · y = 2xy · a

idea at the basis of Differential λ-Calculus (Ehrhard andRegnier, 2003)

In fact analytical functions are smooth: Differential Nets arethe lifting to syntax of these properties

Differential Nets are strongly normalizing: ongoing workwith Michele Pagani

Conclusion

Concluding remarks

∂x2y∂x· a =

∂x · x · y∂x

· a = a · x · y + x · a · y = 2xy · a

Conclusion

Concluding remarks

∂x2y∂x· a =

∂x · x · y∂x

· a = a · x · y + x · a · y = 2xy · a

Conclusion

Concluding remarks

∂x2y∂x· a =

∂x · x · y∂x

· a = a · x · y + x · a · y = 2xy · a

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