This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
Resizing Arrays
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Do curso por Princeton University
Algorithms, Part I
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Stacks and Queues
We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singly-linked list or a resizing array. We introduce two advanced Java features—generics and iterators—that simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems.
Conheça os instrutores
Kevin WayneSenior Lecturer
Computer Science
Robert SedgewickWilliam O. Baker *39 Professor of Computer Science
Computer Science
Okay, our basic array implementation of stacks had the defect where
we required clients to provide the maximum capacity of the stack ahead of time.
Now we're going to look at a technique for resolving that problem.
How do we, we do not implementing the API?
The API says we should just be able to create a stack and
it should be able to grow and shrink to any size.
So how are we going to grow and shrink the array?
Well, first thing you might think of is, when the client pushes a new item onto
the stack, increase the size of the array by 1, and when it pops,
decrease the size of the array by 1.
That's easy to code up, but
not worth it, because it's much too expensive to do that.
The reason is that you have to create a new array, size one bigger, and
copy all the items to that new array.
So, inserting the first N items would take time proportional,
if the stack's of size N-1, it's going to take time N, N-2, time N-1.
So first N items would take the sum of the first N integers,
which we know is about N squared over 2.
Quadratic time to insert N items into a stack,
that kind of performance is unacceptable for
large problems, as we've seen, as we will see many times.
So the challenge is to do the resizing, but
somehow ensure that it happens infrequently.
So the well-known technique for doing that,
called repeated doubling, is to, when the array fills up,
create a new array of twice the size and copy all the items over.
Then we don't create new arrays all that often.
So here's an implementation of that.
We start with an array of size 1.
If we have a full stack, which we know by testing N,
which is the number of items in the stack versus the array length, then
we just resize the array into one of twice the length before inserting the item.
And how do we resize to a new capacity?
We create a new array of that capacity and just go ahead and copy our
current stack into the first half of that and then return it.
And that will reset our instance variable,
which is our stack, to this new, bigger array.
[COUGH] So, the idea and the consequence of this is,
if you insert N items into an array, into a stack with this array representation,
the time will be proportional to N, not N squared.
And the reason is that you only create a new array every time it doubles.
But by the time that it doubles, you've inserted that many items into the stack.
So on average, it's like adding a cost of one to each operation.
So if you just calculate the cost of inserting the first N items,
you're going to have instead of the sum of the integers from to 1 to N,
you're going to have the sum of the powers of 2 from 1 to N.
And that'll give a total cost of about 3N.
So that's, in array accesses for the copy, there's two array accesses.
So to insert N items, it's about three array accesses.
This plot is another way of looking at it,
which is the number of array accesses taken as you implement push operations.
Every time you hit a power of 2, you take that many array accesses, but in a sense,
you've already paid for them by putting those items on the stack.
So that's called amortized analysis,
where we consider the total cost averaged over all operations.
And this is a fine example and useful example,
of amortized analysis to get efficiency in a stack implementation.
Now we have, what about the pop, we have to think about how to shrink the array.
So, you might think, well, we doubled it when it was full,
why don't we cut it in half when it gets to be half full?
We don't want the array to get too empty.
Well, that one doesn't exactly work because of a phenomenon called thrashing.
If the client happens to do push-pop-push-pop alternating when
the array is full, then it's going to be doubling, having, doubling,
having, doubling, having.
Creating new arrays on every operation.
Take time proportional to N for every operation, and
therefore quadratic time for everything.
So I don't want to do that.
The efficient solution is to
wait until the array gets one-quarter full before you have it.
And that's very easy to implement.
We can just test if the array is one quarter full, if it is,
we resize it to half full.
And so then at that point, it's half full, and
it can either grow by adding stuff or shrink by subtracting stuff.
But there won't be another resizing array operation
until it either gets totally full or half again full.
So the invariant of that is that the array is always between 25% and
100% full, number one.
And number two, that every time you resize, you've already paid for
it in an amortized sense by inserting, pushing or popping.
So here's just what happens to the array for
our small client example.
And you can see at the beginning, it doubles from one to two to four, but
once it gets to four, it stays, once it gets to eight, it stays at that size for
awhile even though there's some operations.
And it doesn't shrink back to four until after there's only two items in there,
and then it shrinks, and so forth.
So array resizing doesn't happen that often, but
it's a very effective way of implementing the stack API with
an array where the client does not have to provide the maximum capacity of the stack.
But still, we're guaranteed that the amount of the memory that we are use
is always only a constant multiple of the number of items actually on the stack.
So, the analysis now says that the average running time per
operation for whatever the sequence of operations is,
the average running time is going to be proportional to a constant.
Now, there is a worst case, that is, at the point when the stack doubles,
it takes time proportional to N.
So it's not quite as good performance as we might like.
But the advantage that we get is very fast pushes and
pops, just access array and increment it, and very efficient for most operations.
And for many, many clients, that's an effective tradeoff to make.
So what about memory usage?
Well, this is the analysis of memory usage for
stacks, and it's actually less memory than for strings.
The amount used is between 8N and 32N,
depending on how full the array is and
just a quick analysis of the amount of space that arrays take in Java.
So, again, this analysis is just for the stack itself, not for
the strings, which the client owns.
So, what are the tradeoffs between using a resizing array versus a linked list?
Those are two different implementations of the same API,
and the client can use them interchangeably.
Which one is better?
In many situations, we're going to have multiple implementations of APIs,
and depending on properties of the client program,
we're going to have to choose which one is the better one to use.
So for linked lists, every operation takes constant time in the worst case,
that's a guarantee.
But we have to use a little extra time and space to deal with the links.
So it's going to be slower.
Resizing-array implementation.
We have a good amortized time, so total averaged over the whole process is good.
We have less wasted space and probably faster implementation of each operation.
And so for some clients, maybe that makes a difference.
Perhaps you wouldn't want to use a resizing-array implementation at
the moment that your plane's coming in for a landing.
You wouldn't want it to all of a sudden not implement some operation quickly.
If you need that kind of order, maybe in an internet switch where packets
are coming through at a great rate, you wouldn't want to be in a situation where
you're missing some data because something got slow all of a sudden.
So that's a tradeoff that the client can make.
If I want that guarantee, if I want to be sure that every operation's going to be
fast, I'll use a linked list.
And if I don't need that guarantee, if I just care about the total amount of time,
I'll probably use the resizing-array because the total will be much less,
because individual operations are fast.
So even with these simple data structures, we have really important
tradeoffs that actually make a difference in lots of practical situations.
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