universal approximations in network design rajmohan rajaraman northeastern university based on joint...
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Universal Approximations in Network Design
Rajmohan RajaramanNortheastern University
Based on joint work with Chinmoy Dutta, Lujun Jia, Guolong Lin,
Jaikumar Radhakrishnan, Ravi Sundaram, Emanuele Viola
A day in the life of a courier
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3
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56
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1
1 2 3 4 5 6 7 8
1 20 15 25 40 35 10 10
2 40 10 35 30 20 15
3 30 20 15 10 20
4 10 20 25 20
5 10 35 35
6 25 35
7 10
8
1,2,3,6?2,3,4,5?
1,2,3,6: 20+15+15+30=90
2,3,4,5: 10+10+20+40=80
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1,2,3,6?
2,3,4,5?
1,2,3,6: 20+15+15+30=9020+15+15+30=90 20+40+15+20=95
2,3,4,5: 10+10+20+40=80 40+30+10+35=115
1 2 3 4 5 6 7 8
1 20 15 25 40 35 10 10
2 40 10 35 30 20 15
3 30 20 15 10 20
4 10 20 25 20
5 10 35 35
6 25 35
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8
Stretch ≥ 115/80
Consider tour 1,2,3,4,5,6,7,8
A day in…a lazy courier
Is there a smart lazy courier?
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• Is there a single tour that is universally good?
• Given any subset of the cities, the restricted subtour (preserving the order) is a good approximation to the best tour for that subset
Universal TSP
• Given a metric space (V,d), design a tour T over V that minimizes
– Ts is the sub-tour of T induced by S
– Cost(X) is length of tour X– OPT(S) is cost of an optimal tour for S
€
maxS⊆V
Cost(TS )
OPT(S)
Universal Steiner tree (UST)
Candidate spanning tree TSet S
Cost of inducedSubtree TS = 16
OPT(S) = 6
Stretch ≥ 16/6
Universal Steiner tree (UST)
• Given: A weighted graph G over set V of vertices, and a root vertex
• Goal: Determine a spanning tree T of G that minimizes
– Ts is the subtree of T induced by S and root
– OPT(S) is cost of optimal tree connecting S to root– Cost(X) is the sum of weights of edges in X
• Graphical UST: T only has edges from G• Metric UST: Work with metric completion of G• Graphical UST at least as hard as metric UST
€
maxS⊆V
Cost(TS )OPT(S)
Stretch of T
Universal approximations framework
• Universal version of optimization problem has two additional notions
• Sub-instance relation ≤• Restriction function R: Takes a solution S for
instance I, a sub-instance I’ of I, and returns a solution R(S,I,I’) for I’
• Goal: For given instance I, determine a solution T for I that minimizes
€
maxI '≤I
Cost(R(S,I,I'))
OPT(I')
Universal set cover
• Given set V of elements, collection C of subsets of V, determine f: V C such that – For all x in V, f(x) contains x, and – The following stretch is minimized
€
maxS⊆V
Cost( f (S))
OPT(S)
Motivation
• Optimization under uncertainty– Universal solutions are robust against adversarial
inputs
• Aggregation tree in a sensor network– Data is being generated at several sensors, and
aggregation queries arrive at a sink – Setting up aggregation trees dynamically as
queries and data change may be expensive– Universal Steiner trees provide good
approximations for all query and update patterns
• Universal solutions are differentially private [Bhalgat-Chakrabarty-Khanna 11]
Universal approximation results
Upper bound Lower bound
UTSPO(log4n/loglogn) [JLNRS 05]O(log2n) [GHR 06]O(log n) for doubling metrics
Ω(log n) [GKSS 10, BCK 11]
Ω(log1/6n) Euclidean
Metric UST
O(log4n/loglogn) [JLNRS 05]O(log2n) [GHR 06]O(log n) for doubling metricsO(log n) for planar [KHL 06]
Ω(log n) [JLNRS 05]
Ω(logn/loglog n) Eucl.
Graph UST [DRRSV 11]
for O(1) doubling dim
Same as for metric UST
USCWeighted: O(√nlogn) [JLNRS 05]
Unweighted: O(√n)Ω(√n) [JLNRS 05] €
2˜ O (log3/4 n )
€
2˜ O ( log n )
The roadmap
• Landscape around universal approximations• Universal Steiner trees
– Bounded locally consistent partitions– Metric UST– Graphical UST– Lower bound
• Concluding remarks and open problems
The “universal” landscape• O(log n)-stretch universal TSP for the Euclidean plane
[Platzman-Bartholdi 89]• Simultaneous approximations for single-sink buy-at-
bulk– Given a graph, demands to be routed to a sink, cost for each
edge, route demands to minimize total cost– A single tree is a simultaneous O(1)-approximation for all
concave cost functions [Goel-Estrin 03,…,Goel-Post 10]
• Tree decompositions– [Fakcharoenphol-Rao-Talwar 03] yields metric tree whose
expected stretch for each set is O(log n)– O(log n loglog n) using [Elkin-Emek-Spielman-Teng 06,
Abraham-Bartal-Neiman 09] distribution over spanning trees– The cut-based decompositions of [Räcke 02,08] also aim for a
distribution over trees or tree with prob. embedding
The “universal” landscape
• Oblivious routing and network design– Given graph, source-sink pairs, and per-edge routing cost,
determine routes that are oblivious to demand pairs and cost function
– O(log2n)-approximation for sub-additive cost functions – [Räcke 02, Harrelson-Hildrum-Rao 03, Gupta-Hajiaghayi-
Räcke 06]
• A priori approximations [Schalekamp-Shmoys 08]– For TSP, set of vertices visited drawn from a probability
distribution
• Set covering with eyes closed– Determine a single mapping of elements to sets to minimize
expected cost of covering random element subset– [Grandoni-Gupta-Leonardi-Miettinen-Sankowski-Singh 08]
Universal Steiner tree (UST)
• Given: A weighted graph G over set V of vertices, and a root vertex
• Goal: Determine a spanning tree T of G that minimizes
– Ts is the subtree of T induced by S and root
– OPT(S) is cost of an optimal tree connecting S to root
– Cost(X) is the sum of weights of edges in X€
maxS⊆V
Cost(TS )OPT(S)
Stretch of T
What does a good UST look like?
Candidate spanning tree T
What does a good UST look like?• At each distance level, T
provides a clustering of G• Given tree T, adversary
identifies set S such that– S is “well-separated” in T– S is “close” in G
• To avoid this, UST should cluster nodes so that – Each node’s neighborhood
does not intersect too many clusters
– Otherwise, adversary will select several nodes from this neighborhood lying in different clusters
Bounded locally consistent partitions• A partition of the metric
space with the following properties:– Diameter of every cluster in
partition is at most αR– Every R-ball intersects β
clusters
• Every metric space has an (O(log n),O(log n),R)-partition for every R– Sparse partitions [Awerbuch-
Peleg 90], [Peleg 00]
β = 4
Hierarchical partitions
• A collection of partitions Pi with the following properties:– Partition: Pi is an (α,β,Ri)-
partition
– Hierarchy: Pi is a refinement of Pi+1
– Root padding: Cluster in Pi containing root contains ball of radius Ri around root
• Every metric space has a hierarchical (O(logn), O(logn),O(logn))- partition
A metric UST algorithm [JLNRS 05]
• Compute a hierarchical (O(log n),O(log n),O(log n))-partition
• For each level i, from lowest to highest:– For each level i cluster:
• Select leader from leaders of its constituent level i-1 clusters
• connect level i leader to level i-1 leaders
• Root is always leader of its clusters
Proof sketch for stretch
• To prove: For every set S, Cost(TS) is at most polylog(n) times OPT(S)
• For a level j, cost in UST is O(njlogj+1n):– nj is the number of level-j
ancestors of nodes in S
• Main Lemma: – If Pj is a maximal set of nodes in
S pair-wise separated by logj-1n– Then nj = O(|Pj| log n)
• Cost(OPT(S)) is Ω(|Pj|logj-1n)• Cost at level j in UST is thus
O(log3n)Cost(OPT(S))
Bounding the cost at a level
Proof sketch of Main Lemma:• Any node’s ancestor at level j
is within O(logjn) cost of node• Therefore, O(logjn)-ball
around the ancestors of Pj at level j covers all nj ancestors of S at level j
• By partitioning scheme, it follows that nj is O(|Pj|log n)
Pj is maximal set of nodes in S pair-wise separated by logj-1nnj is the number of nodes at level j of induced tree We have nj = O(|Pj| log n)
Improved bounds for special metrics
• For doubling metrics, the UST algorithm achieves a stretch of O(log n)– Hierarchical (O(1),O(1),O(1))-partition
• Doubling metrics include Euclidean metrics as well as growth-restricted metrics
An O(log2n)-stretch metric UST
• Gupta-Hajiaghayi-Räcke 06• α-padding: A node v is α-padded in a
hierarchical decomposition if– At level i, the ball of radius α2i around v is fully
contained within its cluster at level i
• Theorem: For any v, in any tree drawn from the [FRT 03] distribution, probability that v is Ω(1/logn)-padded is at least 3/4
An O(log2n)-stretch metric UST
• Simple metric UST construction:– Sample O(log n) trees from the FRT distribution– For each vertex v select a tree where v is
Ω(1/logn)-padded– In each tree, build the sub-tree induced by the
root and vertices that selected the tree (using metric completion)
– Return the union of the O(log n) sub-trees computed above
• O(log2n) stretch
Challenges for Graphical UST
• Bounded locally consistent partition:– Partition G into clusters of strong diameter at
most αR– Each R-ball intersects at most β clusters– How small can α and β be? – Open: Is (polylog(n), polylog(n),1)-partitioning
achievable?
• Lemma (Necessity): If σ-stretch achievable for graphical UST, then (σ,σ2,R)-partition exists for all R.
Challenges for Graphical UST
• Hierarchical partition:– Unlike in the metric case, cannot simply elect
leaders and connect directly– Connecting lower level partitions arbitrarily may
introduce huge blowup in costs
• In the [GHR 06] approach:– Can replace the O(log n) FRT trees by the spanning
trees drawn from [EEST 05] distribution– Not clear how to combine paths drawn from these
trees into a single spanning tree
Graphical UST construction
• Construct (2Õ(√logn), 2Õ(√logn),R)-partitions– (O(1),O(1),R) for doubling graphs
• Convert to a hierarchical partitioning:– (2Õ(√logn),2Õ(√logn),2Õ(√logn)) for general graphs– (O(1), O(1), O(log2n)) for doubling graphs
• Build UST from hierarchical partition:– Connect lower-level trees using shortest paths– Invoke properties of partitioning to bound stretch
• for general graphs and 2Õ(√logn) for doubling graphs
• [Dutta-Radhakrishnan-R-Sundaram-Viola 11]
€
2˜ O (log3/4 n )
Lower bound for UST
• Every algorithm for on-line Steiner trees over n nodes has a competitive ratio of (log n) for metrics [Imase-Waxman 91] (log n/loglog n) for Euclidean metrics [Alon-
Azar 92]
• Any UST for an n-node metric space with stretch s(n) can be transformed into an on-line algorithm with competitive ratio of s(n)– Consequence: Every UST has a stretch of (log
n) for n-node metrics, (log n/loglog n) for Euclidean metrics
Complexity of universal problems
• For a given terminal set S:– Finding OPT(S) is NP-hard– Poly-time O(1)-approximations known (Minimum spanning
tree,…,[])
• For a candidate UST, finding the worst-case set is NP-hard
• Finding whether there exists a UST with stretch at most σ is coNP-hard
• Universal problems are “ ”-optimization problems– The -quantification is over an exponential-sized domain
– Lies in ∑2
– Open: is it ∑2-hard?
€
maxS⊆V
Cost(TS )OPT(S)
Open problems
• Close the gaps for UTSP and metric UST– Euclidean UTSP: Ω(log1/6n) vs O(log n)– UTSP: Ω(log n) vs O(log2 n)– Metric UST: Ω(log n) vs O(log2n)
• Is there a polylog(n)-stretch graphical UST?• Strong diameter partitions:
– Can we partition any graph into components of strong diameter polylog(n) such that each vertex has neighbors in polylog(n) components?
– [Peleg 00]
• Universal approximations for other optimization problems