高级算法 (Fall 2018)/Problem Set 1

Problem 1

Recall that in class we show by the probabilistic method how to deduce a [math]\displaystyle{ \frac{n(n-1)}{2} }[/math] upper bound on the number of distinct min-cuts in any multigraph [math]\displaystyle{ G }[/math] with [math]\displaystyle{ n }[/math] vertices from the [math]\displaystyle{ \frac{2}{n(n-1)} }[/math] lower bound for success probability of Karger's min-cut algorithm.

Also recall that the [math]\displaystyle{ FastCut }[/math] algorithm taught in class guarantees to return a min-cut with probability at least [math]\displaystyle{ \Omega(1/\log n) }[/math]. Does this imply a much tighter [math]\displaystyle{ O(\log n) }[/math] upper bound on the number of distinct min-cuts in any multigraph [math]\displaystyle{ G }[/math] with [math]\displaystyle{ n }[/math] vertices? Prove your improved upper bound if your answer is "yes", and give a satisfactory explanation if your answer is "no".

Problem 2

Two rooted trees [math]\displaystyle{ T_1 }[/math] and [math]\displaystyle{ T_2 }[/math] are said to be isomorphic if there exists a bijection [math]\displaystyle{ \phi }[/math] that maps vertices of [math]\displaystyle{ T_1 }[/math] to those of [math]\displaystyle{ T_2 }[/math] satisfying the following condition: for each internal vertex [math]\displaystyle{ v }[/math] of [math]\displaystyle{ T_1 }[/math] with children [math]\displaystyle{ u_1,u_2,\ldots, u_k }[/math], the set of children of vertex [math]\displaystyle{ \phi(v) }[/math] in [math]\displaystyle{ T_2 }[/math] is precisely [math]\displaystyle{ \{\phi(u_1), \phi(u_2),\ldots,\phi(u_k)\} }[/math], no ordering among children assumed.

Give an efficient randomized algorithm with bounded one-sided error (false positive), for testing isomorphism between rooted trees with [math]\displaystyle{ n }[/math] vertices. Analyze your algorithm.

Problem 3

Fix a universe [math]\displaystyle{ U }[/math] and two subset [math]\displaystyle{ A,B \subseteq U }[/math], both with size [math]\displaystyle{ n }[/math]. we create both Bloom filters [math]\displaystyle{ F_A }[/math]([math]\displaystyle{ F_B }[/math]) for [math]\displaystyle{ A }[/math] ([math]\displaystyle{ B }[/math]), using the same number of bits [math]\displaystyle{ m }[/math] and the same [math]\displaystyle{ k }[/math] hash functions.

• Let [math]\displaystyle{ F_C = F_A \and F_B }[/math] be the Bloom filter formed by computing the bitwise AND of [math]\displaystyle{ F_A }[/math] and [math]\displaystyle{ F_B }[/math]. Argue that [math]\displaystyle{ F_C }[/math] may not always be the same as the Bloom filter that are created for [math]\displaystyle{ A\cap B }[/math].
• Bloom filters can be used to estimate set differences. Express the expected number of bits where [math]\displaystyle{ F_A }[/math] and [math]\displaystyle{ F_B }[/math] differ as a function of [math]\displaystyle{ m, n, k }[/math] and [math]\displaystyle{ |A\cap B| }[/math].