15-451 Algorithms 04/29/09
recitation notes
* FFT
* Answer questions, go over topics as needed, general review
* Do TA evals
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FFT
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FFT allows us to do a convolution of two vectors of length n in time
O(n log n). Def of convolution of A and B is vector C such that
C[i] = A[0]*B[i] + A[1]*B[i-1] + ... + A[i]*B[0].
E.g., if A and B are the vectors of coefficients of polynomials A(x)
and B(x), then C gives the coefficients for the polynomial A(x)*B(x).
Uses: Consider the following problem. You are given a string P of 1's and
*'s (the ``pattern''), and a string T of 0's and 1's (the ``text'').
You want to find all places where the pattern P appears in text T,
where a star can match either a 0 or a 1. For instance, if P = 11*1
and T = 10111101, then P appears twice in T: once starting at the
3rd position in T and once starting at the 5th position in T.
Say P has length n and T has length m, where m>n. There is
a simple O(mn)-time algorithm to solve this problem: try all
O(m) possible starting positions, and for each one, check in time
O(n) to see if $P$ matches there.
We can use the FFT to do this faster. All we need to do is reverse P,
change the *'s to zeroes (so in the above example, this would give us
1011), and then do a convolution with T. We can then scan the result
to see the positions where the value of C[i] equals the number of 1s
in P.
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General Review
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What the course has been about:
1. techniques for developing algorithms
2. techniques for analysis
Break down (1) into
(a) fast subroutines: useful tools that go *inside* an algorithm.
E.g., data structures like B-trees, hashing, union-find. Things
like sorting, DFS/BFS.
(b) problems that are important because you can reduce a lot
of *other* problems to them: network flow, linear programming.
Notion of reduction is important in algorithms and in complexity
theory:
- solve a problem by reducing it to something you know how to do.
- show a problem is hard by reducing a known hard problem to it.
Let's organize the algorithms and problems we've discussed in class
along a "running time" line.
sublinear --- linear --- near-linear --- low-degree poly --- general poly(n) --- hard but probably not NP-complete --- NP-complete
sublinear:
- data structures: hashing, balanced search trees, heaps, union find.
linear:
- depth-first search, breadth-first search, topological sorting
- selection/median-finding
near-linear:
- greedy algs with good data structures
- Prim, Kruskal, Dijkstra
- divide-and-conquer algorithms
- sorting, FFTs
low-degree & general poly(n):
- dynamic programming: Bellman-Ford, LCS, etc.
- network flow, matchings, min-cost flow
- linear programming
- Graph matrix algorithms
- primality testing (we didn't really worry about exact running time)
Hard-but-not-probably-not-NP-complete:
- factoring
NP-complete:
- 3-SAT, Vertex cover, Clique, TSP, etc.
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Algorithm tools and where they are typically used:
* Dynamic programming: typically for improving exponential time down to
polynomial.
* Reducing to LP, or reducing to network flow: likewise, typically for
improving exponential time down to polynomial.
* Divide-and-conquer: typically for getting from O(n^2) to O(n log n).
Sometimes just for reducing exponent (like Karatsuba or Strassen).
* Data structures: ditto
* Randomization: everywhere.
* Approximation algorithms: typically for NP-complete problems, but
sometimes also makes sense for easier problems too.
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Analysis tools:
* Amortized analysis, Potential functions, piggy banks:
Typically for showing O(1) or O(log n) amortized cost per operation
* Reductions: proving NP-completeness by reducing a known NP-complete
problem to it, or giving a poly-time algorithm by reducing to a
problem like LP or network flow.
* Recurrences. Esp with divide-and-conquer
* Linearity of expectation
* Group properties for number-theory algs.
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