2. What is an algorithm?
• An algorithm is any well-defined
computational procedure that takes some
value, or set of values, as input and produces
some value, or set of values, as output. An
algorithm is thus a sequence of computational
steps that transform the input into the output.
3. What is an algorithm
Example:
• A procedure for returning web pages
containing given keywords
• A procedure for computing n! given n
• An instruction for assembling a piece of
furniture
• Various sorting and searching algorithms
4. Why care about correctness?
• Suppose your program crashes or gives
incorrect answers 1 time in 100,000
Does this matter for:
• Low usage webpage?
• High usage webpage?
• Banking system?
• Aircraft jet engine control system?
5. Why care about efficiency?
Beware:
• Recent history is that hardware improved rapidly
• This compensated for many poor programs 3GHz
desktop with 2GB RAM on a small program
• almost any implementation will work ‘instantly’
• These might give a false sense that efficiency
does not matter.
6. Why care about efficiency?
But:
• Clock speeds are ‘stuck’
• Not all programs can exploit multi-core, GPGPU,
clouds, but must run on the single core
• Mobile devices are much less powerful
• Also ‘inefficient’ consumes more energy
poorly written apps will drain the mobile device
battery
2% (?) of world electricity goes in server farms
7. Running Time: “finite” but how big?
• Most algorithms transform input data into
output data.
• The running time of an algorithm typically grows
with the input size.
• Average case time is often difficult to determine.
• We (often) focus on the worst case running time
it is easier to analyse
8. Theoretical Analysis
• Uses a “high-level” description of the
algorithm instead of an implementation
• Characterizes running time as a function of
the input size, n.
• Takes into account all possible inputs
• Allows us to evaluate the speed of an
algorithm independently of the
hardware/software environment
9. Pseudocode
• High-level description of an algorithm
• More structured than English prose Less
detailed than a program
• Preferred notation for describing
algorithms
• Hides program design issues
10. Example
• Algorithm arrayMax(A, n)
Input array A of n integers
Output maximum element of A
currentMax = A[0]
for i = 1 to n - 1 do
if A[i] > currentMax then
currentMax = A[i]
return currentMax
11. Primitive Operations
• Basic computations performed by an
algorithm Identifiable in pseudocode
• Largely independent from the programming
language
• Exact definition not important
• Assumed to take a constant amount of time
in the “RAM model”
12. The Random Access Machine
(RAM) Model
• A CPU
• A potentially-unbounded bank of memory cells,
each of which can hold an arbitrary number or
character
• Memory cells are numbered and accessing any
cell in memory takes unit time.
• Note that RAM can stand for both “Random
Access Machine” and “Random Access Memory,”
Which is an unfortunate, but standard, over-
loading.
13. Limitations of RAM model
• “… can hold an arbitrary number …” ?
Can we really expect to store
“9385663592861518003516661777357777771771
773747177174715717777761365661618161616”
in one cell on a real computer?
• Here, we ignore such “bignum” issues: “all
numbers are of equal size, as they all fit in a
single register of the CPU”
• 64bits (signed int) allows up to
9,223,372,036,854,775,807
14. Counting Primitive Operations
• By inspecting the pseudocode, we can
determine the maximum number of primitive
operations executed by an algorithm, as a
function of the input size
Algorithm arrayMax(A, n) # operations
currentMax =A[0] ?
for i = 1 to n - 1 do ?
if A[i] > currentMax then ?
currentMax= A[i] ?
return currentMax ?
Total INCLASS EXERCISE
15. Counting Primitive Operations
• Worst case number of primitive operations
executed as a function of the input size, n
Algorithm arrayMax(A, n) # operations
currentMax =A[0] 2
for i = 1 to n - 1 do 1
if A[i] > currentMax then 2(n-1)
currentMax A[i] 2 (n -1) (worst case)
{ increment counter: i++ } 2 (n - 1) (“hidden”)
{ test counter: i ≤ (n-1) } 2 (n - 1) (“hidden”)
return currentMax 1
Total (8n-4)
16. Counting is “Vague”
• Consider “c =A[i]” then ‘full’ process can be
– get A = pointer to start of array A, and store into a register
– get i, and store into a register
– compute A+i = pointer to location of A[i], and store back
into a register
– get value of “*(A+i)” (from RAM) and store value it into a
register
– Copy the value into the location of c in the RAM
• might not want to count all this, e.g. just count
• ‘plus’ of “A+i”
• the assignment
17. Counting is “Vague” (cont)
• There can be multiple right answers – if you
get‘2’ and I count ‘4’ then it does not mean you
are wrong!
• Note: If I think an answer is ‘4’ then ‘2’ is
probably also acceptable – but “2 n” probably will
not be.
• It is most important to be able to know what is
happening in the underlying process
– be able to link to C and assembly level notions
• • be able to use this to give a reasonably
consistent justification of your answers
