C SC 205 Lecture 4: Runtime Performance
major resources: Data Structures and the Java Collections Framework Second Edition, William Collins, McGraw-Hill, 2005
Introduction to Programming and OO Design using Java, Niño and Hosch, Wiley & Sons, 2002

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Wednesday September 19 (week 2)

Review of Big-O (not Oscar Robertson)

• measure of algorithm not program or computer
• best-, average-, and worst-case behaviors
• counting, coefficients, and dominated terms
• Big O as upper bound -- a O(n) function is also O(n²) -- but we normally use the lowest
• Big Omega as lower bound -- an Omega(n²
) function is also Omega(n)
• Big Theta is both upper and lower bound, there is no containment hierarchy

Big-O performance of various algorithmic sequences

• sequential versus nested loops
• behavior of loop conditions, e.g. sequential versus binary search
• hidden in method calls
• review of a few well known examples: search and sort

Java ArrayList hypothetical implementation

• hypothetical Array implementation
• detemine runtime performance of major operations (get, add, delete)
• hypothetical linked implementation
• concept of a list node
• determine runtime performance of major operations (get, add, delete)