cse 250 lecture 7-9

Fall 2021 ©Oliver Kennedy, Andrew HughesThe University at Buffalo, SUNY 1 / 28

CSE 250Lecture 7-9ADTs, Sequences, Arrays, and Linked ListsTextbook Ch. 1.7.2

This container provides O(1) access to a cat


Sequences

● The Fibonacci Sequence

– 1, 1, 2, 3, 5, 8, 13, 21, 34, 55● Characters of a String

– ‘H’, ‘e’, ‘l’, ‘l’, ‘o’, ‘ ‘, ‘W’, ‘o’, ‘r’, ‘l’, ‘d’● Lines of a file...

A common way to store data is as a sequence


What can you do with a Sequence?

● Get the first element● Enumerate every element of the sequence● Update the n’th element


read nth item

read all items

update nth item

Abstract Data Types (ADTs)

Data in anopaque box

(no specified layout)

Access governed by semantics (what), not implementation (how)


Immutable Sequence ADTs

● apply(idx: Int): A

– Get the element (of type A) at position idx.● iterator: Iterator[A]

– Get access to view all elements in the seq, in order, once.● length: Int

– Count the number of elements in the seq.


Mutable Sequence ADTs

● apply(idx: Int): A

– Get the element (of type A) at position idx.● iterator: Iterator[A]

– Get access to view all elements in the seq, in order, once.● length: Int

– Count the number of elements in the seq.● insert(idx: Int, elem: A): Unit

– Insert the element at position idx with the value elem.● remove(idx: Int): Unit

– Remove the element at position idx.


Implementation via Array

● Positives

– Fast random access– Access as a sequence (default functionality exists)

● Drawbacks

– Fixed size (can’t exceed prespecified size)– “Empty” values (wasted memory)



● Things to think about…

– What happens when we run out of space?– How can an array location be empty?– How are insert and remove handled?

Let’s see an example!



● The Apply Method

– Used to retrieve values in the array– Check if we are out of bounds and raise exception– One apply operation on array: O(1) runtime

● The Update Method

– Used to update values in the array– Check if we are out of bounds and raise exception– One update operation on array: O(1) runtime



● The length method

– Returns the number of allocated elements (not capacity)– Always maintained in a precomputed variable


Indexed Based Operations

● Index-based Insertion

– Check if the position is valid and throw error if not

– Check if the array has capacity and reserve more space if not.

– From end – 1 to idx, shift right by one

– Assign idx the inserted value

– Increase size by 1● Index-based Removal

– Check if the position is valid and throw error if not

– From idx+1 to end, shift left by one

– Decrease size by 1

Worst-case, O(n) runtime


Reserving Space

● Challenge: Allow “Variable-Size” Arrays

– Reserve more space as insertions occur● Allocate new, bigger array (usually doubling capacity)● Copy every element from old to new array● Discard old array

Θ(n) runtimen = number of elements in storage


Inserting at End

● What is the cost to fill an ArrayList?

– Suppose we always insert at the end of the array● with n = the number of elements we’re inserting...

– Worst case, we need to reserve space for each insert● O(n) runtime

– n total insertions● O(n2) runtime


Can we do better than O(n2)

● Example!

– insert(0, )🤺– insert(1, )🤹– insert(2, )🧟– …

● When do we need to reserve memory?

– … always at powers of 2.


Runtime Cost for n Appends

● Cost for adding to end

– nothing to shift right ( Θ(1) )● Cost for reserving space

– potentially O(size) – … but doesn’t occur at every insert

● Only when size = 0 or 2k


Runtime Cost for Appendsi = 1 reserve(1) 0 moved

i = 2 reserve(2) 1 moved


i = 4 ... ...


... ... ...


... ... ...

i = 2j+1 reserve(2j+1) 2j moved

... … ...

i = n depends on n depends on n


Runtime Cost for Appends

● Suppose n = 2k+1 for some k ∈ Z+ (worst case, requires doubling on last insertion)

● Insertion costs

– n operations, O(1) each

● Doubling cost

– Double at every i where i=2j+1 for some j

● First: j = 0● Last: j = k (where k = highest j s.t. 2j < n● Remember n = 2k+1

– Cost to reserve:

● 2i at each doubling


Runtime Cost for Appends

● T(n) = insert cost + reserve cost = Θ(n) + Θ(n) = Θ(n)

● Append runtime is Amortized O(1)

– Runtime for one append is O(n)

– Runtime for n appends is Θ(n)● “Amortized” describes runtime over the long run.

– reserve is only called log(n) times (very infrequently)– Not quite the same as the “average” case

● Average case is the expected runtime over any input● Here, Θ(n) is the runtime.

Amortized ￫ Upfront costs paid off over time


ArrayList

● Pros

– Constant Access/Update Time– Amortized Constant Append

● Cons

– O(n) insert, remove– O(n) for one append


Linked Lists

class SingleLinkedNode[A]( var _data: A, var _next: SingleLinkedNode[A])


Linked List Example

// Allocate space on-demandvar head = new cse250.objects.SingleLinkedNode(1, null)var temp = headtemp.next = new cse250.objects.SingleLinkedNode(2, null)temp = temp._nexttemp.next = new cse250.objects.SingleLinkedNode(3, null)temp = temp._next// Deallocate space on-demandtemp = head._nexthead = temptemp = head._nexthead = temptemp = head._nexthead = temp


Linked Lists

● What if we need to move backwards in the list?

class DoubleLinkedNode[A]( var _data: A, var _prev: DoubleLinkedNode[A], var _next: DoubleLinkedNode[A])


Doubly Linked List Example

// Allocate space on-demandvar head = new cse250.objects.DoubleLinkedNode(1, null, null)var temp = headtemp.next = new cse250.objects.DoubleLinkedNode(2, temp, null)temp = temp._nexttemp.next = new cse250.objects.DoubleLinkedNode(3, temp, null)temp = temp._next// Deallocate space on-demandtemp = head._nexthead = temphead._prev = nulltemp = head._nexthead = temphead._prev = nulltemp = head._nexthead = temp


// Start with a 3-element doubly-linked listvar ptr = head._next // initialize pointer

// Append one elementvar temp = new cse250.objects.DoubleLinkedNode(42, ptr._prev, ptr)ptr._prev = temptemp._prev._next = temp

Positional Access Example


List Container

● Member Variables

– _headNode: First element in the list

– _tailNode: Last element in the list

– _size: Number of nodes in the list

● Positional operations

– insert: Add a value at the provided position

– remove: Remove value at the provided position

● Index-based operations

– apply: return the ith element of the list

– update: modify the ith element of the list

– insert: insert element at the ith position of the list

– remove: remove the element at the ith position in the list


ArrayList vs LinkedList

● ArrayList

– Random Access– Contiguous Memory– Easy to understand implementation

● LinkedList

– Efficient positional access– Allocate on demand


Iteration

● ArrayList

– Iteration is Θ(n)– Access may be faster (‘data locality’)

● LinkedList

– Iteration is Θ(n)– Access may be slower; data scattered around memory


Overview

Function Array LL by Index LL by Position

apply Θ(1) Θ(i) Θ(1)

update Θ(1) Θ(i) Θ(1)

insert O(n) Θ(i) Θ(1)

remove O(n) Θ(i) Θ(1)

append Amortized O(1) Θ(1) Θ(1)

cse 250 lecture 7-9

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