Lecture 1 CMSC 21 PDF

Lecture 1 CMSC 21 Introduction to C++ OUR OLD FRIEND AGAIN, BUT WITH PLUSES What is C++? ▪ It is developed by Bjarne Stroustrup ▪ Originally known as “C with Classes” but later on renamed to “C++” in 1983 ▪ Its first commercial release was in 1985 ▪ C++ > C ▪ Its main features include the General...

Lecture 1 CMSC 21 Introduction to C++ OUR OLD FRIEND AGAIN, BUT WITH PLUSES What is C++? ▪ It is developed by Bjarne Stroustrup ▪ Originally known as “C with Classes” but later on renamed to “C++” in 1983 ▪ Its first commercial release was in 1985 ▪ C++ > C ▪ Its main features include the General Purpose of most programming languages, Object Oriented Programming and its compatibility with C. How much better is C++? PROS CONS Only minor incompatibility with C It is a huge and complex language Proficiency in C++ is a great advantage: Compatibility with C makes it grow some industries requires detached with the purpose of object- programmers' proficiency in this oriented programming particular language and C++ can help you understand other Object-Oriented Languages like Java. How to compile C++? This will be further elaborated in your Laboratory Classes. Pseudocode Pseudocode Conventions Notation Meaning FALSE, TRUE Boolean Constants NIL Unique reference for non-existent objects x←y Assignment x=y Comparison of Equality x ← copy y Copying objects Read me. Comment primitive(x) Primitive routine for object x HELLO-WORLD(x) Algorithmic Function call with parameter x mathematical(x) Mathematical Function with parameter x Notation Meaning 𝑥 Floor Function 𝑥 Ceiling Function 𝑙𝑜𝑔𝑏 𝑥 Logarithm of x base b ln 𝑥 Natural Logarithm of x lg 𝑥 Binary Logarithm of x (Logarithm base 2 of x) max 𝐶 Maximum of a collection; same for min C sin 𝑥 Trigonometric sine; same for other types of trigonometric ratio arcsin 𝑥 Inverse of sine; same for other inverses  Notation Meaning x+y Addition x–y Subtraction x⋅y Multiplication; also can be xy instead x/y Division n div m Integer Division n mod m Integer Modulo not x Logical Negation x and y Logical AND x or y Logical OR x xor y Logical Exclusive-OR x and then y Conditional Logical AND x or else y Conditional Logical OR If-then-else: if Boolean expression then statement0 else if Boolean expression then statement1 else statement2 endif endif Case-of: case expression of constant0: statement0 constant1: statement1 … others: default statement end case For-do: for iteration statement do statements end for While statement: while Boolean Statement do statements end while Repeat-until statement: repeat statements until Boolean Statement Return statement: return expression Error statement: error description Example power(x, y) case y of 0 : return 1 1 : return x others: 𝑧←1 for 𝑖 ← 1 … 𝑦 do 𝑧 ←𝑧∙𝑥 𝑖++ end for return z end case Example INSERTION-SORT(A) for 𝑗 ← 2 … 𝐴. 𝑠𝑖𝑧𝑒 do 𝑘𝑒𝑦 ← 𝐴[𝑗] 𝑖 ←𝑗−1 while 𝑖 > 0 and 𝐴 𝑖 > 𝑘𝑒𝑦 do 𝐴 𝑖+1 ←𝐴 𝑖 𝑖−− end while 𝐴[𝑖 + 1] ← 𝑘𝑒𝑦 end for Example ARRAY-SUM(A) 𝑠𝑢𝑚 ← 0 for all 𝑖 ∈ 𝐴 do 𝑠𝑢𝑚 ← 𝑠𝑢𝑚 + 𝑖 end for return sum Algorithm Analysis and Efficiency Recalling an Algorithm An algorithm is a finite set of instructions to specify a sequence of operations to be carried out in order to solve a specific problem. An algorithm is a procedure for solving a problem in terms of the actions to be executed and the order in which these actions are to be executed. An algorithm must: be complete (all information must be provided) be unambiguous (must only allow one interpretation) terminate after a finite number of operations Why does an algorithm needs to be Efficient? ▪ The most obvious answer is for better performance and optimal usage of resources. How do we measure efficiency? big O, big theta, big omega ▪ Time Complexity (Worst, Average and Best) ▪ Auxiliary Space Complexity ▪ Situation it works best ▪ Comparative Efficiency with peers ▪ Algorithm Paradigm ▪ Case usage Example MERGE SORT INSERTION SORT ❑ 𝑂 𝑛 log 𝑛 , 𝑂 𝑛 log 𝑛 , 𝑂 𝑛 log 𝑛 Time ❑ 𝑂 𝑛2 , 𝑂(𝑛2 ), 𝑂(𝑛) Time ❑ 𝑂 𝑛 Space ❑ 𝑂 1 Space ❑ Works well on Huge n ❑ Works well on Small n ❑ Comparatively efficient ❑ Comparatively inefficient ❑ Divide and Conquer ❑ Incremental Approach Recalling Data Structures A data structure is a way to store and organize data in order to facilitate access and modifications. Examples of data structures: Stacks, Queues, Heaps, Binary Search Trees, etc. NO Data Structure is a perfect fit for all purposes, that is why one must see the importance to know the strengths and weaknesses of each structure and use the most appropriate one for the task. Are all Programming Tasks solvable? Not all problems are solvable using algorithms. An NP-Complete Problem is a problem for which no efficient solution is known. Example: ◦ The Travelling salesman problem (Which is debatable) Why study algorithms and their efficiency? 1. Algorithms are the core of most computer technologies used in contemporary computers. 2. Even an application that does not require algorithmic content at the application level relies heavily upon basic algorithms for other basic functions such as simple i/o functions.