1. Lower Bounds for Property Testing Luca Trevisan U.C. Berkeley

2. Sub-linear Time Algorithms This talk: – algorithms that run in less than linear time (cannot read entire input). –No pre-preprocessing. (Unstructured data) –Must be probabilistic and approximate For optimization problems: –Compute numerical apx of optimum cost (and implicit representation of apx solution?) For decision problems: –What is approximation for decision problems?

3. (Graph) Property Testing Testing a property P with accuracy  in adjacency matrix representation: Given graph G that has property P, accept with probability >3/4 Given graph G that is  -far from property P accept with probability <1/4  -far = must change  –fraction of adjacency matrix to get property P (add/remove >  n 2 edges)

4. Example [GGR,AK] Testing bipartiteness of a given graph G Pick (1/  )polylog(1/  ) vertices, and check if they induce a bipartite graph; if so accept otherwise reject If G is bipartite then alg accepts with prob 1 If G is  -far from bipartite, then whp algorithm discovers an odd cycle (non-trivial to prove) Running time: O ((1/   )polylog(1/  ))

5. Paleontologist’s approach

9. Lower Bounds [BT] Alon-Krivelevich’s algorithm – has one-sided error, is non-adaptive and has running time (1/  2 )polylog(1/  ) Lower Bounds: –  (1/  2 ) for non-adaptive algorithms –  (1/  1.5 ) for adaptive algorithms –Both results hold even for two-sided error

10. Two Distributions Gfar: every edge exists with probability  –whp it is  /3-far from bipartite Gbip: pick a random partition, then every edge that crosses the partition exists with probability 2  Indistinguishable by non-adaptive algorithms making o(1/  2 ) queries Indistinguishable by adaptive algorithms making o(1/  1.5 ) queries

11. Bounded Degree Graphs Testing a property P with accuracy  in adjacency lists representation: Given graph G that has property P, accept with probability >3/4 Given graph G that is  -far from property P accept with probability <1/4  -far = must change  –fraction of adjacency lists entries to get property P (add/remove >  dn edges)

12. Bipartiteness [GR] Testing bipartiteness Repeat polylog n times: –Start at random point, and pick sqrt(n) random walks of length polylog n, if two of them combine to form an odd cycle reject, otherwise accept Analysis: –in a graph where you need to remove constant fraction of edges to make it bipartite, algorithm finds odd cycle

13. Matching Lower Bound [GR] Define two distributions of graphs: –Gfar: a random hamiltonian circuit, plus a random matching (whp 1/100-far from bipartite) –Gbip: a random hamiltonian circuit, plus a random matching conditioned on making the graph bipartite Gfar and Gbip are indistinguishable by algorithms of query complexity o(sqrt(n)).

14. Sublinear Time Approximation Problems restricted to dense instances: Max CUT and other graph problems can be approximated within (1+  ) in graphs with at least  n 2 edges in time 2 poly(1/  ) [GGR] Max 3SAT can be approximated within (1+  ) in instances with at least  n 3 clauses in time 2 poly(1/  ) and similar results for other satisfiability problems [AFKK]

15. Sub-linear Time Approximation Problems on bounded-degree instances Minimum spanning tree –given a connected weighted graph of degree d with weights in range {1,…,w}, can approximate MST weight within (1+  ) in time about O(dw/  2 ) [Chazelle, Rubinfeld, T]

16. General Goals When looking for polynomial-time algorithms: –Several algorithmic techniques of general applicability –A general technique to “prove” impossibility (NP-completeness) For sublinear-time algorithms: –General algorithmic techniques? –Impossibility results?

17. Dense Graphs Some general algorithmic results All problems with a certain logical representation testable in time dependent only on  [AFKS] All regular languages testable in time dependent only on  [AFNS] Only one one-sided error algorithm [GT] (pick a random subgraph and check it is consistent with the property) –Adaptivity does not help –“Only one algorithm” result also for 2-sided error. Few lower bounds

18. Bounded-Degree Graphs Fewer and less general algorithms. Some results are different from dense case adaptivity helps –No property testable with o(sqrt(n)) queries non- adaptive queries. Several problems testable with O(1) adaptive queries. 2-sided better than 1-sided for natural monotone properties –Property “being a forest” has no o(sqrt(n)) one-sided algorithm, but has O(1) two-sided algorithm Few lower bounds

19. Testing 3-Colorability Easy in adjacency matrix representation NP-hard in adjacency list representation Only for small enough  –Can find 3-coloring good for 80% of the edges in a 3- colorable graph using SDP –NP-hard to find 3-coloring good for 98% (?) fraction of edges Implies non-tight, and conditional, lower bound for query complexity

20. Other problems The query complexity of following problems is equivalent to query complexity of testing 3col –Testing satisfiability of 3SAT instance Every variable occurs in O(1) clauses, “adjacency list” representation –Approximating max cut, vertex cover, independent set,..., in bounded-degree graphs –Approximating Max SAT, Max 2SAT,... Lower bound of sqrt(n) for all problems implied by [GR] lower bound for testing bipartiteness

21. Some Results from [BOT] For one-sided error algorithms: –  (n) query complexity to distinguish 3-colorable graphs from graphs that are (1/3 –  )-far –Lower bound applies to testing problems that are solvable in polynomial time For two-sided error algorithms: –For some ,  (n) query complexity to distinguish 3-colorable graphs from graphs that are  -far.

22. Additional Results Unconditionally, algorithms running in time o(n) cannot: –Approximate Max 3SAT better than 7/8 –Approximate Max Cut in bounded-degree graphs better than 16/17 –... Hastad’97 proved above problems are NP-hard

23. The 3-Coloring Lower Bound Consider first one-sided error algorithms It’s enough to find a graph G that is (1/3 –  )-far from 3-colorable, but every subgraph of size <  n is 3-colorable –(for every  there is an  such that...) Then an algorithm of query complexity <  n either accepts G (which is wrong) or rejects some 3-colorable graph (which means the algorithm has not one-sided error)

24. The Graph Pick a graph of degree O(1/  2 ) at random (pick so many random matchings) Then it is (1/3 –  )-far whp But, for some , whp, every subgraph induced by k <  n vertices contains <1.5k edges In a minimal non-3-colorable graph, every vertex has degree at least 3 Every subgraph induced by <  n vertices is 3- colorable [Erdos]

25. Explicit Construction Can the previous construction be derandomized? For constants d, , , and for every suff large n, we can explicitly construct a graph –on n vertices, with max degree d, –  -far from 3-colorable, –every subset of  n vertices induces a 3-colorable subgraph.

26. Explicit Construction We construct a 3SAT formula such that for constants k,  ’,  ’ –Every variable occurs k times –No assignment satisfies more than 1-  ’ fraction of clauses –Every  ’ fraction of clauses is satisfiable –Then we use (slightly new) reduction from 3SAT to 3Coloring

27. The Formula Fix a degree-d expander graph G=(V,E) such that for every cut (S,V-S) at least min{|S|,|V-S|} edges cross the cut (enough d=14) Have two variables x uv and x vu for each egde (u,v) For every vertex v have the (3SAT equivalent of) the constraint –  u x uv = 1 +  w x vw

28. Structure of the Analysis Impossible to satisfy more than a fraction 1/(d+1) of the constraints Can always satisfy half of the constraint –define an auxiliary network –show that the auxiliary network has no small cut because of expansion –then there is a large flow –use large flow to find assignment for subset of constraint

29. Flow Argument Want to satisfy constraints corresponding to vertices in C, with |C| < |V|/2 s t V-C C Construct flow network with new source s, sink t obtained by collapsing V-C, and vertices in C

30. Flow Argument s A C-A t |A| edges |C-A| edges Every cut has size at least |C| There is a 0/1 flow of cost at least |C| Interpreted as an assignment, satisfies all constraints in C

31. Two-Sided Error Algorithms Need to define two distributions of graphs Gcol and Gfar such that: Graphs in Gcol are (almost) always 3-colorable Graphs in Gfar are (almost) always far from 3-colorable To an algorithm of bounded query complexity, Gcol and Gfar look (almost) the same

32. Main Step Define two distributions Dsat and Dfar of instances of E3LIN-2 (systems over GF(2) with 3 variables per equation) –Systems in Dsat are always satisfiable –Systems in Dfar are (almost) always (1/2-  )-far from satisfiable –To an algorithm of bounded query complexity, Dsat and Dfar look the same We get Gcol and Gfar using reduction from approximate E3LIN-2 to approximate 3-coloring

33. E3LIN-2 X1 + X3 + X10 = 0 mod 2 X2 + X3 + X4 = 1 mod 2 X1 + X2 + X9 = 0 mod 2...

34. Main Building Block We show that for every c there is  such that there exists a left-hand side with –n variables, cn equations, 3 variables per equations, every variable occurs in 3c equations –every  n equations are linearly independent Pick the left-hand side at random –repeat 3c times: pick at random a set of n/3 disjoint triples of variables Explicit construction?

35. Distributions The left-hand side is always as before In Dsat, we pick a random assignment to the variables, and set right-hand side consistently –always satisfiable In Dfar, we pick the right-hand side uniformly at random –With high probability, (1/2 – O(1/sqrt c))-far

36. Indistinguishability Two distributions differ only in right-hand side In Dfar uniformly distributed In Dsat,  n-wise independent –Linear independence implies statistical independence Look the same to algorithm that sees less than  n equations

37. Conclusion of the Argument No algorithm of “query complexity” o(n) can distinguish satisfiable instances of E3LIN-2 from instances that are (1/2-  )-far from satisfiable For some , no algorithm of query complexity o(n) can distinguish 3-colorable graphs from graphs that  –far from 3-col. No algorithm of query complexity o(n) can approximate Max 3SAT better than 7/8...

38. Open Questions Show that distinguishing 3-colorable graphs from (1/3-  )-far graphs requires query complexity  (n) –we can only prove it for one-sided error Show that approximating Max SAT better than ¾ and Max CUT bettter than ½ requires query complexity  (n) –we only know  (sqrt(n)) [implicit in GR] –would “explain” why we need SDP

39. Some more open questions In adjacency matrix representation, most interesting problems solvable in constant (in  ) time For some problems (eg testing triangle- freeness) analysis uses Szemeredy’s regularity lemma, and constant is hyper-exponential in  Lower bound (1/  ) log 1/  and only and for one- sided error Alternative analysis / stronger lower bounds?