PROBLEM-SOLVING STRATEGIES:- Problem-solving strategies defined, Importance of understanding multiple problem-solving strategies, Trial and Error, Heuristics, Means-Ends Analysis, and Backtracking (Working backward). THE PROBLEM-SOLVING PROCESS:- Computer as a model of
computation, Understanding the problem, Formulating a model, Developing
an algorithm, Writing the program, Testing the program, and Evaluating the
solution.
ALGORITHM AND PSEUDOCODE REPRESENTATION:- Meaning and Definition of Pseudocode, Reasons for using pseudocode, The main constructs of pseudocode - Sequencing, selection (if-else structure, case structure) and
repetition (for, while, repeat-until loops), Sample problems*
FLOWCHARTS** :- Symbols used in creating a Flowchart - start and end, arithmetic calculations, input/output operation, decision (selection), module name (call), for loop (Hexagon), flow-lines, on-page connector, off-page connector.
SELECTION AND ITERATION USING PYTHON:- if-else, elif, for loop, range, while loop. Sequence data types in Python - list, tuple, set, strings, dictionary, Creating and using Arrays in Python (using Numpy library).
DECOMPOSITION AND MODULARIZATION* :- Problem decomposition as a strategy for solving complex problems, Modularization, Motivation for modularization, Defining and using functions in Python, Functions with multiple return values RECURSION:- Recursion Defined, Reasons for using Recursion, The Call Stack, Recursion and the Stack, Avoiding Circularity in Recursion, Sample problems - Finding the nth Fibonacci number, greatest common divisor of two positive integers,
the factorial of a positive integer, adding two positive integers, the sum of digits of a positive number.
COMPUTATIONAL APPROACHES TO PROBLEM-SOLVING
(Introductory diagrammatic/algorithmic explanations only. Analysis not required) :-
Brute-force Approach - - Example: Padlock, Password guessing
Divide-and-conquer Approach - - Example: The Merge Sort Algorithm - Advantages of Divide and Conquer Approach - Disadvantages of Divide and Conquer Approach
Dynamic Programming Approach - Example: Fibonacci series - Recursion vs Dynamic Programming
Greedy Algorithm Approach - Example: Given an array of positive integers each indicating the completion
time for a task, find the maximum number of tasks that can be completed in
the limited amount of time that you have. - Motivations for the Greedy Approach - Characteristics of the Greedy Algorithm - Greedy Algorithms vs Dynamic Programming
Randomized Approach - Examples, Motivations for the Randomized Approach
