1. Models of Greedy Algorithms for Graph Problems Sashka Davis, UCSD Russell Impagliazzo, UCSD SIAM SODA 2004

2. Why greedy algorithms? Greedy algorithms are simple, and have efficient implementations They are used as: –Exact algorithms for many optimization problems –Approximation schemes with guaranteed approximation ratios for hard problems –As heuristics for hard optimization problems

3. Goal To design an abstract model of greedy algorithms and answer the questions: 1.Could a known greedy approximation algorithm be improved? 2.Can we prove lower bounds on approximation ratio of greedy algorithms for hard problems? 3.Can we formalize the intuition that greedy algorithms are weaker than other algorithmic paradigms?

4. History 1.[BNR02] defined Priority algorithm framework for scheduling problems 2.[AB02] defined Priority algorithms for facility location and set cover 3.[BL03] proved bounds on performance of Priority algorithms for VC, IS and Coloring

5. General results Extended the work of [BNR02] and [AB02] and defined problem-independent model for greedy algorithms Defined a formal model of Memoryless priority algorithms Characterization of the power of Fixed, Memoryless, and Adaptive algorithms in terms of combinatorial games

6. Separations Dynamic Programming Algorithms Adaptive Priority Algorithms Fixed Priority Algorithms Memoryless Priority Algorithms   

7. Results for graph problems Shortest paths –No Fixed priority algorithm can achieve any constant approximation ratio, for ShortPath problem on graphs, with non-negative weights –No Adaptive priority algorithm can achieve any constant approximation ratio, for ShortPath problem on graphs with negative weights, but no negaitve weight cycles Fixed Priority Algorithms Adaptive Priority Algorithms  Adaptive Priority Algorithms Dynamic Programming Algorithms 

8. Results for specific graph problems Shortest paths –No Fixed priority algorithm can achieve any constant approximation ratio, for ShortPath problem on graphs, with non-negative weights –No Adaptive priority algorithm can achieve any constant approximation ratio, for ShortPath problem on graphs with negative weights, but no negaitve weight cycles

9. Steiner trees –Proved lower bound of 1.18 on the approximation ratio achieved by Adaptive priority algorithms –Improved adaptive priority algorithm, achieving an approximation ratio 1.875 for special metric instances where the distance between nodes is [1,2] Results for specific graph problems

10. Weighted Vertex Cover –Proved lower bound 2 for Adaptive priority algorithms, matching the standard 2-approximation scheme Independent Set –Proved lower bound of 3/2 on the performance of Adaptive algorithms for degree-3 graphs Results for specific graph problems

11. Remainder of the talk What is a greedy algorithm? Formal definitions of Fixed and Adaptive priority algorithms and show a strong separation between Fixed and Adaptive priority algorithms Lower bound 2 on the approximation ratio of Adaptive priority algorithms on weighted vertex cover problem Fixed Priority Algorithms Adaptive Priority Algorithms 

12. What is a greedy algorithm? Given a universe of data items  1.The instance is a set of data items, subset of  2.The algorithm defines an ordering function on  and views the data items in the instance in that order 3.The algorithm makes an irrevocable decision for each data item, which depends only on data items seen so far, and decisions made so far, not future data items 4.The solution is decisions made on each data item

13. Kruskal algorithm for MST Input (G=(V,E), ω: E → R ) 1.Initialize empty solution T 2.L = Sorted list of edges in non-decreasing order according to their weight 3.while (L is not empty) –e = next edge in L –Add the edge to T, as long as T remains a forest and remove e from L 4.Output T

14.  is a set of data items ;  is a set of options Input: instance I={  1,  2,…,  n }, I   Output: solution S={(  i,  i ) | i= 1,2,…,d};  i   1. Determine an ordering function π :  → R +  { ∞ } 2. Order I according to π (  ) 3. Repeat –Let the next data item in the ordering π be  i –Make a decision  i   –Update the partial solution S until (decisions are made for all data items) 4. Output S={(  i,  i ) | i= 1,2,…,d} Fixed priority algorithms

15. Kruskal is a Fixed priority algorithm  is a set of edges Each edge is represented as (u, v, ω)  ={accepted, rejected} Priority function π : (u, v, ω)= ω

16. Question: Can all problems with known greedy algorithms be solved by a Fixed priority algorithm? ShortPath Problem: Given a graph G=(V,E), ω: E →R + ; s, t  V. Find a directed tree of edges, rooted at s, such that the combined weight of the path from s to t is minimal Fixed Priority Algorithms ShortPath ?

17. Answer: Theorem: No Fixed priority algorithm can achieve any constant approximation ratio for the ShortPath problem Fixed Priority Algorithms ShortPath

18. Fixed priority game SolverAdversary γdγd γiγi γ3γ3 γjγj γkγk γ2γ2 γ1γ1 Γ0Γ0 S_sol = {(γ i2,σ i2 )} σ i2 S_sol = {(γ i2,σ i2 ), (γ i4,σ i4 )} γ i2 γ i9,…γ i1 γ i3 γ i4 γ i5 γ i6 γ i7 γ i8 Γ0Γ0 Γ1Γ1 Γ2Γ2 σ i4 Γ3Γ3 End Game S_adv = {(γ i2,σ * i2 ), (γ i4,σ * i4 )} Solver is awarded =∅

19. Fixed priority game for ShortPath problem Data items are edges of the graph Decision options = {accept, reject} A strategy for the Adversary in the game establishes a lower bound on approximation ratio achieved by any Fixed priority algorithm

20. Adversary selects  0 t b s a u(k) w(k) x(1) v(1) y(1) z(1)

21. Solver selects an order on  0 If then the Adversary presents: t b s a u(k) w(k) x(1) v(1) y(1) z(1)

22. Adversary’s strategy Waits until Solver considers edge y(1) Solver will consider y(1) before z(1) Event 1 σ y =accept Event 2 σ y =reject

23. Event 1: Solver accepts y(1) t u(k) x(1) y(1) z(1) b a s The Solver constructs a path {u,y} The Adversary outputs solution {x,z}

24. Event 2: Solver rejects y(1) The Solver fails to construct a path. The Adversary outputs a solution {u,y}. t u(k) x(1) y(1) z(1) b a s

25. The outcome of the game: The Solver either fails to output a solution or achieves an approximation ratio (k+1)/2 The Adversary can set k arbitrarily large and thus can force the Algorithm to claim arbitrarily large approximation ratio

26. Conclusion No Fixed priority algorithm can achieve any constant approximation ratio for the ShortPath problem Dijkstra algorithm solves the ShortPath problem exactly Dijkstra algorithm (G=(V,E), s  V) T← ∅; S←{s}; Until (S≠V) Find e=(u,x) | e = min e  Cut(S, V-S) {path(s, x)+ω(e)} T← T  {e}; S ← S  {x}

27. Adaptive priority algorithms  is a set of data items;  is a set of options Input: instance I={  1,  2,…,  d }, I  Output: solution S={(  i,  i ) | i= 1, 2,…,d} 1. Initialization U= ∅; S=∅;I *= ∅;t=1 2. Repeat Determine an ordering function π t :  -I * → R +  { ∞ } Pick the highest priority data item  t  U according to π t Make an irrevocable decision  t   Update U ← U-{  t }; S←S  {(  t,  t )}; I * ←I *  {  t }; t←t+1 until (decisions are made for all data items) 3. Output S={(  i,  i ) | i= 1,2,…,d}

28. Dijkstra is an Adaptive priority algorithm –  is a set of edges; Each edge is represented as (u, v, ω) –  ={accepted, rejected} –ordering Adaptive Priority Fixed Priority ShortPath Fixed Priority Algorithms Adaptive Priority Algorithms 

29. Weighted Vertex Cover [Joh74] gave a greedy 2-approximation algorithm Can we design an improved approximation algorithm in the class of Adaptive priority algorithms?

30. [Joh74] greedy 2-approximation for WVC Input: instance I={  1,  2,…,  n } Output: solution S  I 1. Initialization U= ∅; S=∅;I *= ∅;t=1 2. Repeat π t (  ) = ω(v)/(|adj_list – I * |) Order all  t  U according to their π t value in non- decreasing order. Let  t be first data item in the order. if (π t (  ) ≠ ∞ ) then –Make an irrevocable decision  t = in –Update U ← U-{  t }; S←S  {  t }; I * ←I *  {  t }; t←t+1 until (π t (  ) ≠ ∞ ) 3. Output S

31. Priority model for WVC problem A data item is a vertex of the graph  = (v, ω(v), [adj_list] ); ω:V → R The input to the algorithm is a set of data items I={  1,  2,…,  n } Set of decision options is Σ={accept, reject} Ordering function: π t (  ) = ω(v)/(|adj_list – I * |) For simplicity, the solution will be a vertex cover S  I, of vertices accepted by the algorithm.

32. Question Is there an Adaptive priority algorithm which gives a better for the WVC than the known 2-approximation? –No! Theorem: No Adaptive priority algorithm can achieve an approximation ration better than 2

33. Adaptive priority game SolverAdversary γ3γ3 γ5γ5 γ6γ6 γ1γ1 γ4γ4 γ7γ7 γ2γ2 S_sol = {(γ 7,σ 7 )} σ4σ4 S_sol = {(γ 7,σ 7 ), (γ 4,σ 4 )} Γ3Γ3 Γ1Γ1 Γ2Γ2 σ7σ7 The Game Ends : 1.S_adv = {(γ 7,σ * 7 ), (γ 4,σ * 4 ),(γ 2,σ * 2 )} 2.Solver is awarded payoff f(S_sol)/f(S_adv) γ8γ8 γ9γ9 γ 10 γ 11 γ 12 Γ0Γ0 σ2σ2 S_sol = {(γ 7,σ 7 ), (γ 4,σ 4 ),(γ 2,σ 2 )} 

34. The Adversary chooses instances to be graphs K n,n The weight function ω:V→ {1, n 2 } n2n2 1n2n2 n2n2 n2n2 11 1

35. The game Data items –each node appears in  o as two separate data items with weights 1, n 2 Solver moves –Choses a data item, and commits to a decision Adversary move –Removes from the next  t the data item, corresponding to the node just committed and..

36. Adversary’s strategy is to wait unitl Event 1: Solver accepts a node of weight n 2 Event 2: Solver rejects a node of any weight Event 3: Solver has committed to all but one nodes on either side of the bipartite 1 1 1 1 1

37. Event 1: Solver accepts a node ω(v)=n 2 The Adversary chooses part B of the bipartite as a cover, and incurs cost n The cost of a cover for the Solver is at least n 2 +n-1 1 1 n2n2 1 1 1 1

38. Event 2: Solver rejects a node of any weight The Adversary chooses part A of the bipartite as a cover. The Solver must choose part B of the bipartite as a cover. n2n2 n2n2

39. Event 3: Solver commits to n-1 nodes w(v)=1, on either side of K n,n The Adversary chooses part B of the bipartite as a cover, and incurs cost n The cost of a cover for the Solver is 2n-1 1 1 1 1 1 1 1 n2n2

40. Summary No Adaptive priority algorithm can achieve an approximation ratio better than 2 for the WVC The known 2-approximation is optimal and cannot be improved

41. Conclusions 1.The class of Adaptive priority algorithms is more powerful 2.The known 2-approximation for the WVC is optimal in the class of Adaptive priority algorithms and cannot be improved Fixed Priority Algorithms Adaptive Priority Algorithms 

42. Future research directions Extend the framework to capture larger class of greedy algorithms –Define a notion of global information –Redefine the notion of local information Build formal models for backtracking and dynamic programming algorithms and evaluate their performance on hard problems