The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

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来自 Stanford University 的课程

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

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The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

从本节课中

Week 3

Huffman codes; introduction to dynamic programming.

- Tim RoughgardenProfessor

Computer Science

So we clearly have something to be excited about.

There's clearly this opportunity to design binary prefix-free codes which

improve over the obvious fixed link solution.

So, we'd like to have in some sense, optimal algorithm for this problem and

for that, we of course need a crisp problem definition.

So, to do that it turns out to be useful to think of codes as binary trees.

So, this video will develop that connection concluding with the final

formal problem statement. So, the last video introduced us to this

very interesting computational problem, namely given characters from an alphabet

with frequencies, find the best binary prefix-free encoding,

find the code which minimizes the average number of bits needed to encode a

character. Crucial, the reasoning about this problem

is thinking of binary codes as binary trees. So to give you an idea about this

correspondence, let's just revisit three of the binary codes we saw in the

previous video and see what kind of trees they correspond to.

So let's just continue with the four symbol alphabet A B C D.

The obvious fixed length code where we encode A, B, C, D was 0, 0, 0, 1, 1, 0

and 1, 1 is just going to correspond to the complete binary tree with four

leaves. So let me label this complete binary tree

as follows. I'm going to label the leaves A through D from left to right, and I'm

going to label each edge of the tree with a 0 if it corresponds to a left-child

relationship and with a 1 if it corresponds to a right-child

relationship. And now what you see is there's a

correspondence between the bits along root to leaf paths and the fixed length

encoding. So for example for the symbol C, if we

follow the path from the root to the leaf labeled C, we first encounter a 1 because

it's a right-child, then we encounter a 0 because it's a left-child.

That gives us a 1 and a 0, that's the same as our encoding of the

symbol C in this fixed length encoding and the same of course is true for the

other three leaves. Next, when we first started playing

around with variable-length encodings to motivate the prefix-free property, we

studied a code where we replaced the double 0 for an A with a single 0 and the

double 1 for a D with a single 1. Now this code was not prefix-free,

but we can still represent it as a binary tree.

It's just not going to be a complete one. So I'm going to label the edges of this

tree the same way as before. Left-child edges will be given a label of

0, right-child edges will be given a label

of 1. I'm going to label the left and right

children of the root A and D respectively and the two leaves will be given the

labels B and C. The reason I labeled the nodes in this

way is, because then we have the same kind of correspondence between the

encodings that we proposed for the various symbols and the bits that you see

along a path from the root to nodes with those symbols.

So for example, if you at the node labeled D, the path from the root only

has a single bit 1 and that coincides with the proposed encoding of the symbol

D. Now, remember, this code is not

prefix-free and so therefore, as we saw, it had ambiguity.

So if you're wondering what some encoded message means and you see a 0, you're not

sure that 0 might be representing the symbol A or alternatively it might be the

first half of an encoding of the symbol B.

So, this ambiguity is also noticeable in the tree.

And the property in the tree that tips you off to the ambiguity is that there

are symbols at internal nodes of the tree.

The symbols are not merely at the leaves as they were with the first tree with the

fixed length in coding, but there are also two internal nodes

that have symbols. So let's draw the tree for our final

example which, was the variable length but prefix-free code that we looked at in

the previous video. So this is going to correspond to a tree

which is not perfectly balanced, but it will have labels only at the leaves of

the tree. So, if you label the edges of this tree

the way we've been doing, all the left-child edges get a 0, all the

right-left edges get a 1, and we label the leaves of the tree from

A to D going from left to right. You'll see we have the same

correspondence as in the previous two trees.

the sequence of bits from the root to a leaf coincide with the proposed encoding

for it. So, for example, if you look at the leaf

labeled C, because you traverse a right-child, another right-child and a

left-child to get to it, that's the sequence 1, 1, 0 and that is precisely

the proposed encoding for the symbol C. So in general, any binary code can be

expressed as a tree in this way, with the left-child pointers being labeled with

0's, the right child pointers being labeled with 1's,

and the various nodes being labeled the symbols of the given alphabets, and the

bits from the root down to the node labeled with the given symbol

corresponding to the proposed encoding for that symbol.

And what's cool about thinking about codes as trees is that the really

important prefix-free condition, which seems like a nuisance to check in the

abstract, shows up in a really clean way in these trees,

namely the prefix-free condition is the same as leaves being the only nodes that

have labels. No internal nodes are allowed to have a

label in a prefix-free code. The reason for this is that we've set it

up so that the encodings correspond to the bits along paths from the root to the

labeled node. So being a prefix of another corresponds

to one node being an ancestor of the other, and so, if all the labels are at

the leaves, then of course nobody is an ancestor of the other and we have no

prefixes. The other things that's really cool about

this tree representation of codes is, it becomes pictorially obvious how you do

the coding given this sequence of 0's and 1's from a prefix-free binary code,

namely, you start at the beginning and you start at the root of your tree,

whenever you see a 0 you go left, whenever you see a 1 you go right.

At some point you'll hit a leaf, that leaf will have some label and that's the

symbol that's being encoded, and after you hit a leaf, you just start all over

again back to the root. So for example, if you were using our

variable-length prefix-free code for the four letter alphabet, as in our running

example, and you were given the sequence of 0s and

1s, 0, 1, 1, 0, 1, 1, 1. What would you do?

Well, you'd start at the root and you see a 0, so you follow the left-child

pointer, and you immediately get to a leaf.

It's labeled A, so you're going to output and A as the first symbol.

Now you start all over. You return to the root, now what do you see?

You see a 1, so you go right, you see another one, so you go right, and now you

see a 0, so you go left and that gets you to the leaf labeled C.

Now you start all over again. You see a 1, you go right, you see a 1,

you go right again, and then, finally you see yet one more 1 and you wind up at the

lead labelled D. So by repeated traversals through the

tree, you decode the sequence of 0s and 1s as a C, D.

There's never any ambiguity, because when you hit a label, you know you're going to

leave, there's no where to go. And every internal note, it's unlabeled,

you know to expect another bit, another traversal further down in the tree.

So a final important point about this correspondence is that the encoding

lengths of the symbols, the number of bits needed to encode the various

symbols, are just the depths of the corresponding leaves in the tree that

corresponds to the code. So for example, in our running example

the symbol A is the only one that needs only one bit to encode and it's also the

only leaf in level one of the tree. And similarly B needs two bits and shows

up in the next level, the C and the D which need three bits

show up in the third level. So this correspondence is, really just by

construction, so how do you encode a given symbol.

Well, it's just the bits on the path from the root, and that the number of such

bits is just the number of pointer traversals you need to get from the root

down to that leaf, and that's just the depth of that leaf in the tree.

So we're now in a great position to really have a crisp definition of the

problem. The input of course is just the

frequencies for a bunch different symbols i from some alphabet capital sigma.

I'm going to use P sub i as notation for the frequency of symbol i.

So we know what it is we want to optimize.

We want to minimize the expected number of bits needed to encode a symbol, where

the average of the expectation is taken over the provided frequencies of the

various symbols. Well, let's express this objective

function using our newfound correspondence with binary trees.

In particular, the fact that we can think about encoding lengths as depths of

leaves in trees. So, given a tree, T, which corresponds to

a prefix-free binary code. That is it should be a binary tree and

the leaves of this tree should be in one-to-one correspondence with the

symbols of sigma. We're going to define capital L(T) as the

average encoding length. It's an average in the sense that we sum

over all of the symbols of the alphabet, we weight each symbol by the frequency,

and remember, this is part of the input, so whether the provided frequency P

survived that symbol i. And then how many bits do we need to

encode that symbol i? Well, it's just the depth of the leaf

which is labelled i in the given tree, capital T.

So this is what we want to make as small as possible.

So, for instance, using the data from the previous video, the letters A, B, C, D

with frequencies 60, 25, 10, and 5%. Then if we use the complete binary tree,

that is the fixed length encoding, we just get two bits per character.

While if we use the lopsided tree optimized, so that each A only takes one

bit while suffering three bits for C and D, then the average encoding length drops

to 1.55, as we saw in the last video. So what then is the goal,

what's the responsibility of our algorithm?

Well, amongst all binary trees, which have leaves and correspondence to the

symbols of sigma, we want to compute the one which makes this average encoding

length as small as possible which minimizes our objective function capital

L. Turns out Huffman's greedy algorithm does

it. More details to come.

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