submodular set function maximization via the multilinear relaxation & dependent rounding chandra...
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![Page 1: Submodular Set Function Maximization via the Multilinear Relaxation & Dependent Rounding Chandra Chekuri Univ. of Illinois, Urbana-Champaign](https://reader036.vdocument.in/reader036/viewer/2022062318/551b015455034607418b4a1c/html5/thumbnails/1.jpg)
Submodular Set Function Maximizationvia the Multilinear Relaxation & Dependent
Rounding
Chandra ChekuriUniv. of Illinois, Urbana-Champaign
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Max weight independent set
• N a finite ground set
• w : N ! R+ weights on N
• I µ 2N is an independence family of subsets• I is downward closed: A 2 I and B ½ A ) B 2 I
max w(S)
s.t S 2 I
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Independence families
• stable sets in graphs
• matchings in graphs and hypergraphs
• matroids and intersection of matroids
• packing problems: feasible {0,1} solutions to A x · b where A is a non-negative matrix
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Max weight independent set
• max weight stable set in graphs
• max weight matchings
• max weight independent set in a matroid
• max weight independent set in intersection of two matroids
• max profit knapsack
• etc
max w(S)
s.t S 2 I
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This talk
f is a non-negative submodular set function on N
Motivation:• several applications• mathematical interest
max f(S)
s.t. S 2 I
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Submodular Set Functions
A function f : 2N ! R+ is submodular if
A Bj
f(A+j) – f(A) ¸ f(B+j) – f(B) for all A ½ B, i 2 N\B
f(A+j) – f(A) ≥ f(A+i+j) – f(A+i) for all A N , i, j N\A
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Submodular Set Functions
A function f : 2N ! R+ is submodular if
A Bj
f(A+j) – f(A) ¸ f(B+j) – f(B) for all A ½ B, i 2 N\B
f(A+j) – f(A) ≥ f(A+i+j) – f(A+i) for all A N , i, j N\A
Equivalently: f(A) + f(B) ≥ f(AB) + f(AB) 8 A,B N
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• G=(V,E) undirected graph
• f : 2V ! R+ where f(S) = |δ(S)|
Cut functions in graphs
S
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Coverage in Set Systems
• X1, X2, ..., Xn subsets of set U
• f : 2{1,2, ..., n} ! R+ where f(A) = |[ i in A Xi |
X1
X2X3
X4
X5
X1
X2X3
X4
X5
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Submodular Set Functions
• Non-negative submodular set functions
f(A) ≥ 0 8 A ) f(A) + f(B) ¸ f(A[ B) (sub-additive)
• Monotone submodular set functions
f(ϕ) = 0 and f(A) ≤ f(B) for all A B
• Symmetric submodular set functionsf(A) = f(N\A) for all A
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Other examples
• Cut functions in hypergraphs (symmetric non-negative)
• Cut functions in directed graphs (non-negative)
• Rank functions of matroids (monotone)
• Generalizations of coverage in set systems (monotone)
• Entropy/mutual information of a set of random variables
• ...
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Example: Max-Cut
• f is cut function of a given graph G=(V,E)
• I = 2V : unconstrained
• NP-Hard
max f(S)
s.t S 2 I
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Example: Max k-Coverage
• X1,X2,...,Xn subsets of U and integer k
• N = {1,2,...,n}
• f is the set coverage function (monotone)
• I = { A µ N : |A| · k } (cardinality constraint)
• NP-Hard
max f(S)
s.t S 2 I
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Approximation Algorithms
A is an approx. alg. for a maximization problem:
• A runs in polynomial time
• for all instances I of the problem A(I) ¸ ® OPT(I) ® (· 1) is the worst-case approximation ratio of A
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Techniques
f is a non-negative submodular set function on N
• Greedy
• Local Search
• Multilinear relaxation and rounding
max f(S)
s.t. S 2 I
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Greedy and Local-Search
[Nemhauser-Wolsey-Fisher’78, Fisher-Nemhauser-Wolsey’78]
• Work well for “combinatorial” constraints: matroids, intersection of matroids and generalizations
• Recent work shows applicability to non-monotone functions [Feige-Mirrokni-Vondrak’07] [Lee-Mirrokni-Nagarajan-Sviridenko’08] [Lee-Sviridenko-Vondrak’09] [Gupta etal, 2010]
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Motivation for mathematical programming approach
• Quest for optimal results
• Greedy/local search not so easy to adapt for packing constraints of the form Ax · b
• Known advantages of geometric and continuous optimization methods and the polyhedral approach
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Math. Programming approach
max w(S)
s.t S 2 I
max w¢x
s.t x 2 P(I)
Exact algorithm: P(I) = convexhull( {1S : S 2 I})
xi 2 [0,1] indicator variable for i
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Math. Programming approach
max w(S)
s.t S 2 I
max w¢x
s.t x 2 P(I)
Round x* 2 P(I) to S* 2 I
Exact algorithm: P(I) = convexhull( {1S : S 2 I})
Approx. algorithm: P(I) ¾ convexhull( {1S : S 2 I})
P(I) solvable: can do linear optimization over it
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Math. Programming approach
max f(S)
s.t S 2 I
max F(x)
s.t x 2 P(I)
Round x* 2 P(I) to S* 2 I
P(I) ¶ convexhull( {1S : S 2 I}) and solvable
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Math. Programming approach
• What is the continuous extension F ?
• How to optimize with objective F ?
• How do we round ?
max f(S)
s.t S 2 I
max F(x)
s.t x 2 P(I)
Round x* 2 P(I) to S* 2 I
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Some results
[Calinescu-C-Pal-Vondrak’07]+[Vondrak’08]=[CCPV’09]
Theorem: There is a randomized (1-1/e) ' 0.632 approximation for maximizing a monotone f subject to any matroid constraint.
[C-Vondrak-Zenklusen’09]
Theorem: (1-1/e-²)-approximation for monotone f subject to a matroid and a constant number of packing/knapsack constraints.
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What is special about 1-1/e?
Greedy gives (1-1/e)-approximation for the problem max { f(S) | |S| · k } when f is monotone [NWF’78]• Obtaining a (1-1/e + ²)-approximation requires
exponentially many value queries to f [FNW’78]
• Unless P=NP no (1-1/e +²)-approximation for special case of Max k-Coverage [Feige’98]
New results give (1-1/e) for any matroid constraint improving ½ . Moreover, algorithm is interesting and techniques have been quite useful.
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Submodular Welfare Problem
• n items/goods (N) to be allocated to k players
• each player has a submodular utility function fi(Ai) is the utility to i if Ai is allocation to i)
• Goal: maximize welfare of allocation i fi(Ai)
Can be reduced to a single f and a (partition) matroid constraint and hence (1-1/e) approximation
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Some more results
[C-Vondrak-Zenklusen’11]
• Extend approach to non-monotone f
• Rounding framework via contention resolution schemes
• Several results from framework including the ability to handle intersection of different types of constraints
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Math. Programming approach
• What is the continuous extension F ?
• How to optimize with objective F ?
• How do we round ?
max f(S)
s.t S 2 I
max F(x)
s.t x 2 P(I)
Round x* 2 P(I) to S* 2 I
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Multilinear extension of f
[CCPV’07] inspired by [Ageev-Sviridenko]
For f : 2N ! R+ define F : [0,1]N ! R+ as
x = (x1, x2, ..., xn) [0,1]N
R: random set, include i independently with prob. xi
F(x) = E[ f(R) ] = S N f(S) i S xi i N\S (1-xi)
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Why multilinear extension?
• Ideally a concave extension to maximize
• Could choose (“standard”) concave closure f+ of f
• Evaluating f+(x) is NP-Hard!
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Properties of F
• F(x) can be evaluated (approximately) by random sampling
• F is a smooth submodular function• 2F/xixj ≤ 0 for all i,j.
Recall f(A+j) – f(A) ≥ f(A+i+j) – f(A+i) for all A, i, j
• F is concave along any non-negative direction vector
• F/xi ≥ 0 for all i if f is monotone
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Math. Programming approach
• What is the continuous extension F ? ✔
• How to optimize with objective F ?
• How do we round ?
max f(S)
s.t S 2 I
max F(x)
s.t x 2 P(I)
Round x* 2 P(I) to S* 2 I
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Maximizing F
max { F(x) | xi · k, xi 2 [0,1] } is NP-Hard
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Approximately maximizing F
[Vondrak’08]
Theorem: For any monotone f, there is a (1-1/e) approximation for the problem max { F(x) | x P } where P [0,1]N is any solvable polytope.
Algorithm: Continuous-Greedy
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Approximately maximizing F
[C-Vondrak-Zenklusen’11]
Theorem: For any non-negative f, there is a ¼ approximation for the problem max { F(x) | x P } where P [0,1]n is any down-closed solvable polytope.
Remark: 0.325-approximation can be obtained
Algorithm: Local-Search variants
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Local-Search based algorithm
Problem: max { F(x) | x 2 P }, P is down-monotone
x* = a local optimum of F in P
Q = { z 2 P | z · 1-x* }
y* = a local optimum of F in Q
Output better of x* and y*
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Local-Search based algorithm
Problem: max { F(x) | x 2 P }, P is down-monotone
x* = a local optimum of F in P
Q = { z 2 P | z · 1-x* }
y* = a local optimum of F in Q
Output better of x* and y*
Theorem: Above algorithm gives a ¼ approximation.
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Math. Programming approach
• What is the continuous extension F ? ✔
• How to optimize with objective F ? ✔
• How do we round ?
max f(S)
s.t S 2 I
max F(x)
s.t x 2 P(I)
Round x* 2 P(I) to S* 2 I
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Rounding
Rounding and approximation depend on I and P(I)
Two results:
• For matroid polytope a special rounding
• A general approach via contention resolution schemes
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Rounding in Matroids
Matroid M = (N, I)
Independence polytope: P(M) = convhull({1S | S 2 I})
given by following system [Edmonds]
i 2 S xi · rankM(S) 8 S µ N
x 2 [0,1]N
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Rounding in Matroids
[Calinescu-C-Pal-Vondrak’07]
Theorem: Given any point x in P(M), there is a randomized polynomial time algorithm to round x to a vertex x* (hence an indep set of M) such that • E[x*] = x• F(x*) ≥ F(x)
[C-Vondrak-Zenklusen’09]
Different rounding with additional properties and apps.
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Rounding
F(x*) = E[f(R)] where R is obtained by independently rounding each i with probability x*
i
R unlikely to be in I
max F(x)
s.t x 2 P(I)
Round x* 2 P(I)to S* 2 I
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Rounding
F(x*) = E[f(R)] where R is obtained by independently rounding each i with probability x*
i
R unlikely to be in I
Obtain R’ µ R s.t. R’ 2 I and E[f(R’)] ¸ c f(R)
max F(x)
s.t x 2 P(I)
Round x* 2 P(I)to S* 2 I
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A simple question?
0.9
1
0.4
0.6
0.4
1
0.7
0.7
0.3
0.6
0.1
• x is a convex combination of spanning trees
• R: pick each e 2 E independently with probability xe
Question: what is the expected size of a maximal forest in R? (n - # of connected components)
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A simple question?
• x is a convex combination of spanning trees of G
• R: pick each e 2 E independently with probability xe
Question: what is the expected size of a maximal forest in R? (n - # of connected components)
Answer: ¸ (1-1/e) (n-1)
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Related question
• x is a convex combination of spanning trees of G
• R: pick each e 2 E independently with probability xe
Want a (random) forest R’ µ R s.t. for every edge e
Pr[e 2 R’ | e 2 R] ¸ c
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Related question
• x is a convex combination of spanning trees of G
• R: pick each e 2 E independently with probability xe
Want a (random) forest R’ µ R s.t. for every edge e
Pr[e 2 R’ | e 2 R] ¸ c
) there is a forest of size e c xe = c (n-1) in R
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Related question
• x is a convex combination of spanning trees of G
• R: pick each e 2 E independently with probability xe
Want a (random) forest R’ µ R s.t. for every edge e
Pr[e 2 R’ | e 2 R] ¸ c
Theorem: c = (1-1/e) is achievable & optimal [CVZ’11]
(true for any matroid)
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Contention Resolution Schemes
• I an independence family on N
• P(I) a relaxation for I and x 2 P(I)
• R: random set from independent rounding of x
CR scheme for P(I): given x, R outputs R’ µ R s.t.
1. R’ 2 I
2. and for all i, Pr[i 2 R’ | i 2 R] ¸ c
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Rounding and CR schemes
Theorem: A monotone CR scheme for P(I) can be used to round s.t.
E[f(S*)] ¸ c F(x*)
Via FKG inequality
max F(x)
s.t x 2 P(I)
Round x* 2 P(I)to S* 2 I
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Remarks
[CVZ’11]
• Several existing rounding schemes are CR schemes
• CR schemes for different constraints can be combined for their intersection
• CR schemes through correlation gap and LP duality
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Math. Programming approach
Problem reduced to finding a good relaxation P(I) and a contention resolution scheme for P(I)
max f(S)
s.t S 2 I
max F(x)
s.t x 2 P(I)
Round x* 2 P(I) to S* 2 I
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Concluding Remarks
• Substantial progress on submodular function maximization problems in the last few years
• New tools and connections including a general framework via the multilinear relaxation
• Increased awareness and more applications
• Several open problems still remain
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Thanks!