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LABORATOIRE d’ANALYSE et d’ARCHITECTURE des SYSTEMES INTEGER PROGRAMMING, DUALITY and SUPERADDITIVE FUNCTIONS Jean B. LASSERRE IMA workshop, July 2005 1

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### Transcript of INTEGER PROGRAMMING, DUALITY and SUPERADDITIVE …INTEGER PROGRAMMING, DUALITY and SUPERADDITIVE... LABORATOIRE d’ANALYSE et d’ARCHITECTURE des SYSTEMES

INTEGER PROGRAMMING, DUALITY and

Jean B. LASSERRE

IMA workshop, July 2005

1 The integer program

maxx c′ x | A x = b; x ∈ Nn

with A = [A1, . . . , An] ∈ Zm×n, b ∈ Zm has a dual problem

D : minf∈Γ f(b) | f(Aj) ≥ cj, j = 1, . . . , n

where Γ is the space of functions f : Rm → R that are superad-

ditive (f(a + b) ≥ f(a) + f(b)), and with f(0) = 0. (Jeroslow,

Wolsey, etc ...)

Elegant but rather abstract and not very practical; however, used

to derive valid inequalities. Moreover, the celebrated Gomory

cuts used in Solvers (CPLEX, XPRESS-MP, ..) can be inter-

2 D : minf∈Γ f(b) | f(Aj) ≥ cj, j = 1, . . . , n

is too rich, and somehow, a rephrasing of the problem: Indeed,

the set Γ contains in particular the optimal value function f :

Zm → R ∪ −∞

b 7→ f(b) := maxx c′x | Ax = b; x ∈ Nn

Similarly, the standard LP problem maxxc′x |A x = b; x ≥ 0 has

an abstract dual problem

DL : minf∈∆

f(b) | f(Aj) ≥ cj, j = 1, . . . , n

where ∆ is now the set fo functions f : Rm → R ∪ −∞ that

are concave (or even concave piecewise linear).

3 But the simpler linear program

D∗L : minf linear

f(b) | f(Aj) ≥ cj, j = 1, . . . , n

that is,

D∗L : minλ b′λ | A′j λ ≥ cj, j = 1, . . . , n

is a valid dual ... for a fixed value of b. It does not use concave

piecewise linear functions (but does not contain the optimal value

function b 7→ f(b) := maxx≥0 c′x |A x = b).

Hence, for integer programs, one should also obtain a dual sim-

pler than the superadditive dual ....

4 CONTINUOUS OPTIM. DISCRETE OPTIM.− | −

f(b, c) := max c′x

s.t.

[Ax ≤ b

x ∈ Rn+

←→

fd(b, c) := max c′x

s.t.

[Ax ≤ b

x ∈ Nn

l − lINTEGRATION SUMMATION

− | −f(b, c) :=

∫Ω

ec′xdx

Ω :=

[Ax ≤ b

x ∈ Rn+

←→fd(b, c) :=

∑Ω

ec′x

Ω :=

[Ax ≤ b

x ∈ Nn

5 ef(b,c) = limr→∞ f(b, rc)1/r; efd(b,c) = lim

r→∞ fd(b, rc)1/r.

or, equivalently

f(b, c) = limr→∞

1

rln f(b, rc); fd(b, c) = lim

r→∞1

rln fd(b, rc).

6 CONTINUOUS OPTIM. DISCRETE OPTIM.− | −

Legendre-FenchelDuality ←→ ??

l − l

INTEGRATION SUMMATION− | −

Laplace-Transform Z-TransformDuality ←→ Duality

7 Legendre-Fenchel duality : f : Rn → R convex; f∗ : Rn → R.

λ 7→ f∗(λ) = F(f)(λ) := supyλ′y − f(y).

(One-sided) Laplace-Transform: f : Rn+ → R; F : Cn → C.

λ 7→ F (λ) = L(f)(λ) :=∫Rn

+

e−λ′y f(y) dy.

(One-sided) Z-Transform: f : Zn+ → R; F : Cn → C.

λ 7→ F (z) = Z(f)(z) :=∑

m∈Zn+

z−mf(m).

8 with Ω := x ∈ Rn | A x ≤ y; x ≥ 0

Fenchel-duality: Laplace-duality

f(y, c) := maxx∈Ω

c′x f(y, c) :=∫Ω

ec′x dx

f∗(λ, c) := minyλ′y − f(y, c) F (λ, c) :=

∫e−λy f(y, c) dy

=∫x≥0

ec′x[∫

Ax≤ye−λy dy

]dx

=1

m∏j=1

λj

n∏k=1

(A′λ− c)k

with : A′λ− c ≥ 0; λ ≥ 0 with : <(A′λ− c) > 0; <(λ) > 09 In standard form, with Ω := x ∈ Rn | A x = b; x ≥ 0

F (λ, c) =1

n∏k=1

(A′λ− c)k

and one retrieves f(y, c) by

f(y, c) =∫Γ

e y′ λ F (λ, c) dλ =∫Γ

e y′ λ∏nj=1(A

′ λ− c)jdλ

Integration by Cauchy’s residue techniques →• The (multidimensional) poles λ of F solve πσ Aσ = cσ for all

bases Aσ of the continuous LP, ... which yields ....

10 Brion and Vergne ’s continuous formula

Terminology of LP in standard form:

Let Aσ := [Aσ1| . . . |Aσm] be a basis of maxc′x|Ax = b;x ≥ 0,with x(σ) the corresponding vertex, πσ := c′σA−1

σ the associated

dual variable and the reduced cost vector ck − πσAk, k 6∈ σ.

Then :

f(b, c) =∑

x(σ): vertex of Ω(b)

ec′x(σ)

det(Aσ)∏k 6∈σ

(−ck + πσAk)

from which it easily follows that

log[lim

r→∞ f(b, rc)]1/r

= maxx(σ) : vertex of Ω(b)

c′ x(σ).

11 Discrete Z-duality

Let Ω(y) := x ∈ Rn | A x = y; x ≥ 0.

fd(y, c) :=∑

x∈Ω(y)∩Zn

ec′x

Then the generating function or Z-transform of fd reads

z 7→ Fd(z, c) :=∑

y∈Zm

z−y fd(y, c),

and, by simple algebra

Fd(z, c) =n∏

j=1

1

1− ecj z−Aj(=

n∏j=1

1

1− ecj z1−A1j . . . zm

−Amj)

with |zAj | > ecj ∀j = 1, . . . , n.

12 To compute fd(y, c) it then suffices to invert the generating func-

tion, that is:

fd(y, c) =∫|z|=γ

zy−1Fd(z, c) dz

which can be done by repeated application of Cauchy’ s residue

Theorem, which in turn requires computing the poles of the

generating function Fd.

• The Poles of Fd are also associated with the bases of the LP

max c′ x |A x = y, x ≥ 0 !!

13 Let σ := [Aσ1| · · · |Aσm] be a feasible basis of the LP

maxxc′ x |A x = y, x ≥ 0, with µ(σ) := det(Aσ), and associated

“dual” variables πσ, which are solutions of πAσ = cσ.

Indeed, each basis σ provides µ(σ) complex poles z = eλ in

Cm, which are solutions of the polynomial equations eA′σλ = ecσ.

(Compare with πσAσ = cσ)

The µ(σ) solutions z = eλ are of the form

λ = πσ + 2iπv

µ(σ); v ∈ Vσ ⊂ Zm

Vσ = v ∈ Zm | v′Aσ = 0 mod µ(σ)

14 Brion and Vergne ’s discrete formula

Re-interpreted with these data, Brion and Vergne ’s original dis-

fd(y, c) =∑

x(σ): vertex of Ω(y)

ec′ x(σ) ×

1

µ(σ)

v∈Vσ

e2iπv′ y/µ(σ)∏k 6∈σ

(1− e−(2iπv′Ak/µ(σ)) e(ck−πσAk)

)

15 Back to optimization : fd(b, c) = max c′x |A x = b; x ∈ Nn.

Theorem . Assume that

maxσ

ec′x(σ) × limr→∞

v∈Vσ

e2iπv′b/µ(σ)∏k 6∈σ

(1− e−(2iπv′Ak/µ(σ)) er(ck−πσAk))

1/r

is attained at a unique basis σ∗. Then :

fd(b, c) = c′ x(σ∗) +∑

k 6∈σ∗(ck − πσ∗Ak)x∗k =

∑j

cj x∗j .

σ∗ is an optimal basis of the linear program, and x(σ∗) (resp. x∗)is an optimal solution of the linear (resp. integer) program.

16 In this case :

fd(b, c) = c′x(σ∗) +

max∑

k 6∈σ∗(ck − πσ∗Ak)xk

Aσ∗ u +∑

k 6∈σ∗Ak xk = b

u ∈ Zm; xk ∈ N ∀k 6∈ σ∗

fd(b, c) = c′x(σ∗) + ρ := opt. value of GOMORY relaxation!

17 limr→∞

v∈Vσ

e2iπv′b/µ(σ)∏k 6∈σ

(1− e−(2iπv′Ak/µ(σ)) er(ck−πσAk))

1/r

is the same as detecting the leading term uρ of the rational

fraction

u 7→

v∈Vσ

e2iπv′b/µ(σ)∏k 6∈σ

(1− e−(2iπv′Ak/µ(σ)) u(ck−πσAk))

as u→∞.

18 With u := er, fd(y, rc) is the rational fraction

u 7→∑

x(σ): vertex of Ω(y)

uc′ x(σ) ×

1

µ(σ)

v∈Vσ

e2iπv′ y/µ(σ)∏k 6∈σ

(1− e−(2iπv′Ak/µ(σ)) u(ck−πσAk)

)

=∑

bases σ of Ω(y)

gσ(u)

When Gomory relaxation is not exact, the leading term as u→∞is not unique and cancellations occur.

19 A Discrete Farkas Lemma

Let A ∈ Nm×n, b ∈ Nm and consider the problem deciding whether

or not A x = b has a solution x ∈ Nn, or, equivalently, deciding

whether or not fd(b, c) ≥ 1.

Theorem: (i) A x = b has a solution x ∈ Nn if and only if the

polynomial b 7→ zb − 1 in R[z1, . . . , zm] can be written

zb − 1 =n∑

j=1

Qj(z)(zAj − 1) (=

n∑j=1

Qj(z)(zA1j1 . . . z

Amjm − 1)

for some polynomials z 7→ Qj(z) with nonnegative coefficients.

(ii) The degree of the Qj’s is bounded bym∑

j=1

bj −maxk

m∑j=1

Ajk.

20 A single LP to solve with n×(b∗+m

b∗)

variables,(b∗+m

m

)constraints

and a (sparse) matrix of coefficients in 0,±1

One also retrieves the classical Farkas Lemma in Rn, that is,

x ∈ Rn |A x = b;x ≥ 0 6= ∅ ⇔[A′ u ≥ 0 ⇒ b′ u ≥ 0

]Indeed, if A x = b has a solution x ∈ Nn, then with u = ln z,

eb′u − 1 =n∑

j=1

Qj(eu1, . . . , eum)(e(A′u)j − 1).

Therefore,

A′u ≥ 0 ⇒ e(A′ u)j − 1 ≥ 0 ⇒ eb′u − 1 ≥ 0 ⇒ b′u ≥ 0,

and so A x = b has a nonnegative solution x ∈ Rn+.

21 The general case A ∈ Zm×n, b ∈ Zm.

Let Ω := x ∈ Rn|Ax = b; x ≥ 0 be a polytope.

Let α ∈ Nn be such that for every column Aj of A,

Akj+αj≥ 0 ∀ k = 1, . . . , m; let N 3 β ≥ ρ(α) := maxα′x |x ∈ Ω.

Theorem: (i) Ax = b has a solution x ∈ Nn if and only if thepolynomial z 7→ zb(zy)β − 1 in R[z1, . . . , zm] can be written

zb(zy)β − 1 = Q0(z, y)(zy − 1) +n∑

j=1

Qj(z, y)(zAj(zy)αj − 1),

for some polynomials Qj(z), all with nonnegative coefficients.

(ii) The degree of the Qj’s is bounded by b∗ := (m+1)β+m∑

j=1

bj.

22 Back to standard Farkas lemma

x ∈ Rn |A x = b; x ≥ 0 6= ∅ ⇔[A′λ ≥ 0⇒ b′λ ≥ 0

]

But, equivalently x ∈ Rn |A x = b, x ≥ 0 6= ∅ if and only if thepolynomial λ 7→ b′λ can be written

b′ λ =n∑

j=1

Qj(λ)(A′ λ)j,

for some polynomials Qj ⊂ R[λ1, . . . , λm], all with nonnegativecoefficients.

In this case, each Qj is necessarily a constant, that is, Qj ≡Qj(0) = xj ≥ 0, and A x = b!

23 P = x ∈ Rn |Ax = b, x ≥ 0 P ∩ Zn

x ∈ P x ∈ integer hull (P)⇔ x = Q(0, . . . ,0) with ⇔ x = Q(1, . . . ,1) with

Q ∈ R[λ1, . . . , λm] Q ∈ R[eλ1, . . . , eλm]

b′λ = 〈Q, A′ λ〉 eb′ λ − 1 = 〈Q, eA′ λ − 1n〉

Q 0 Q 0

Comparing continuous and discrete Farkas lemma An equivalent Linear program

Let 0 ≤ q = qjα ∈ Rns be the coefficients of the Qj’s in

zb − 1 =n∑

j=1

Qj(z) (zAj − 1)

They are solutions of a linear system

M q = r, q ≥ 0

for some matrix M and vector r, both with 0,±1 coefficients.

** M and r are easily obtained from A, b with no computation

Write q = (q1, q2, . . . , qn) with each qj = qjα ∈ Rs, and let

cjα := cj for all α

25 Theorem : Let A ∈ Nm×n, b ∈ Nm, c ∈ Rn.

(i) The integer program P → maxx c′x | A x = b, x ∈ Nn has

same value as the linear program

Q→ maxq

n∑j=1

c′j qj | M q = r; q ≥ 0.

(ii) Let q∗ be an optimal vertex, and let

x∗j :=∑α

q∗jα = Qj(1) j = 1, . . . , n.

Then x∗ ∈ Nn and x∗ is an optimal solution of P.

26 The LP-dual Q∗ of the linear program Q reads

Q∗ → minππ′ r | M′ π ≥ c.

More precisely, with D :=∏n

j=10,1, . . . , bj ⊂ Nm,

Q∗ →

min

ππ(b)− π(0)

s.t. π(Aj + α)− π(α) ≥ cj, α + Aj ∈ D, j = 1, . . . , n

Let Π := π : D → R, and for every π ∈ Π, let fπ : D → R be

the function

fπ(x) := infx+α∈D

π(x + α)− π(α), x ∈ D

27 For every π ∈ Π, the function fπ is superadditive and fπ(0) = 0.

With Q∗ one may then associate the optimization problem

S∗ →

minπ∈Π

fπ(b)

s.t. fπ(Aj) ≥ cj, j = 1, . . . , n.

Thus, S∗ is a simplified and explicit form of the abstract super-

f : D → R, instead of f : Nm → R ∪ −∞.

It is the analogue for IP with A x = b of Wolsey’s dual for IP with

A x ≤ b. Note that Q∗ is simpler than S∗.

28 Moreover Q∗ is simpler than S∗ as in the LP Q∗, the functionπ : D → R is NOT required to be super additive!! In S∗ on hasto write the O(|D|2) superadditivity constraints

π(x + α)− π(α) ≥ π(x), x, α ∈ D, with x + α ∈ D,

and the n additional constraints π(Aj) ≥ cj, j = 1, . . . , n.

On the other hand, in Q∗ one only has the n O(|D|) constraints

π(Aj + α)− π(α) ≥ cj α ∈ D, with Aj + α ∈ D,

For instance, in the unbounded knapsack problem

maxxc′ x |

n∑j=1

aj xj = b; x ∈ Nn

One has n + b2/2 constraints in S∗, and nb−∑n

j=1 aj in Q∗. With P = x ∈ Rn+ |A x = b, the integer hull co(P ∩ Zn) reads

co(P ∩ Zn) = x ∈ Rn | −n∑

j=1

λ∗j xj ≤ b′π∗,

for finitely many (π∗, λ∗), generators of the convex cone Ω ⊂R|D| ×Rn

Ω :=(π, λ) ∈ R|D| ×Rn | π(α + Aj)− π(α) + λj ≥ 0,

α + Aj ∈ D, j = 1, . . . n

Equivalently, again with

x 7→ fπ∗(x) := infα+x∈D

π∗(x + α)− π∗(α), x ∈ D

co(P ∩ Zn) = x ∈ Rn |n∑

j=1

fπ∗(Aj)xj ≤ fπ∗(b),

29 CONCLUSION

Generating functions permit to exhibit a natural duality for

integer programming, an IP-analogue of LP duality.

This duality permits to simplify the abstract superadditive dual,

and so, might help providing efficient Gomory cuts in MIP

solvers like CPLEX, or XPRESS-MP.

30 LasserreJ.B. (2005). Integer Programming, duality and super-

additive functions, Cont. Math 474, pp. 138-150

Lasserre J.B. (2004). Integer Programming Duality. in: Trends

in Optimization. Proc. Symp. Appl. Math. 61, pp. 67–83

Lasserre J.B. (2004). The integer hull of a convex rational poly-

tope. Discr. Comp. Geom 32, pp. 129–139.

Lasserre J.B. (2004). Generating functions and duality for inte-

ger programs. Discrete Optim. 1, pp. 167–187.

Lasserre J.B. (2004). A discrete Farkas lemma. Discrete Opti-

mization 1, pp. 67-75. . Another dual problem

Let A ∈ Zm×n, b ∈ Zm, c ∈ Rn. Let y 7→ f(y, c) = maxc′x |Ax =y; x ≥ 0. The Fenchel transform (−f)∗ of the convex function−f(., c) is the convex function

λ 7→ (−f)∗(λ, c) = supy∈Rm

λ′y + f(y, c).

The dual problem of the linear program is obtained from Fenchelduality as

f(b, c) = infλ∈Rm

b′ λ + (−f)∗(−λ, c)

= infλ∈Rm

b′ λ + supx∈Rm

+

(c−A′λ)′x = minλb′ λ |A′ λ ≥ c

Equivalently

ef(b,c) = infλ∈Rm

supx∈Rm

+

e(b−A x)′λec′ x

32 Define

ρ∗ := infz∈Cm

supx∈Nn

<(zb−A x ec′ x

)= inf

z∈Cmf∗d(z, c).

Hence,

f∗d(z, c) = <

zbn∏

j=1

(z−Ajecj)xj

< ∞ if |zAj | ≥ ecj ∀j

(that is, A′ ln |z| ≥ c). Next, (writing z ∈ C as eλeiθ)

ρ∗ ≤ infz∈Rm

supx∈Rn

+

<(zb−Ax ec′x

)= inf

λ∈Rmsup

x∈Rn+

e(b−Ax)′λ ec′x = ef(b,c)

Finally, with z ∈ Cm arbitrary fixed

supx∈Nn

<(zb−Ax ec′x

)≥ ec′x∗ = efd(b,c)

Hence fd(b, c) ≤ ln ρ∗ ≤ f(b, c).

33 Theorem: Let σ∗ be an optimal basis of the linear program.

Under uniqueness of the “maxσ” in Brion and Vergne ’s formula,

efd(b,c) = ρ∗ = maxx∈Nn

<(zb−A xec′ x

)= f∗d(z, c)

where zAj = γ ecj ∀j ∈ σ∗ for some real γ > 1.

z is an optimal solution of the dual problem

infz∈Cm

f∗d(z, c)

34