- Design and Analysis of Algorithms
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- Basics of Algorithms
- DAA - Introduction
- DAA - Analysis of Algorithms
- DAA - Methodology of Analysis
- Asymptotic Notations & Apriori Analysis
- DAA - Space Complexities

- Design Strategies
- DAA - Divide & Conquer
- DAA - Max-Min Problem
- DAA - Merge Sort
- DAA - Binary Search
- Strassen’s Matrix Multiplication
- DAA - Greedy Method
- DAA - Fractional Knapsack
- DAA - Job Sequencing with Deadline
- DAA - Optimal Merge Pattern
- DAA - Dynamic Programming
- DAA - 0-1 Knapsack
- Longest Common Subsequence

- Graph Theory
- DAA - Spanning Tree
- DAA - Shortest Paths
- DAA - Multistage Graph
- Travelling Salesman Problem
- Optimal Cost Binary Search Trees

- Heap Algorithms
- DAA - Binary Heap
- DAA - Insert Method
- DAA - Heapify Method
- DAA - Extract Method

- Sorting Methods
- DAA - Bubble Sort
- DAA - Insertion Sort
- DAA - Selection Sort
- DAA - Quick Sort
- DAA - Radix Sort

- Complexity Theory
- Deterministic vs. Nondeterministic Computations
- DAA - Max Cliques
- DAA - Vertex Cover
- DAA - P and NP Class
- DAA - Cook’s Theorem
- NP Hard & NP-Complete Classes
- DAA - Hill Climbing Algorithm

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In this chapter, we will discuss the complexity of computational problems with respect to the amount of space an algorithm requires.

Space complexity shares many of the features of time complexity and serves as a further way of classifying problems according to their computational difficulties.

Space complexity is a function describing the amount of memory (space) an algorithm takes in terms of the amount of input to the algorithm.

We often speak of **extra memory** needed, not counting the memory needed to store the input itself. Again, we use natural (but fixed-length) units to measure this.

We can use bytes, but it's easier to use, say, the number of integers used, the number of fixed-sized structures, etc.

In the end, the function we come up with will be independent of the actual number of bytes needed to represent the unit.

Space complexity is sometimes ignored because the space used is minimal and/or obvious, however sometimes it becomes as important issue as time complexity

Let **M** be a deterministic **Turing machine (TM)** that halts on all inputs. The space complexity of **M** is the function $f \colon N \rightarrow N$, where ** f(n)** is the maximum number of cells of tape and

We estimate the space complexity of Turing machine by using asymptotic notation.

Let $f \colon N \rightarrow R^+$ be a function. The space complexity classes can be defined as follows −

*SPACE = {L | L is a language decided by an O(f(n)) space deterministic TM}*

*SPACE = {L | L is a language decided by an O(f(n)) space non-deterministic TM}*

**PSPACE** is the class of languages that are decidable in polynomial space on a deterministic Turing machine.

In other words, *PSPACE = U _{k} SPACE (n^{k})*

One of the earliest theorem related to space complexity is Savitch’s theorem. According to this theorem, a deterministic machine can simulate non-deterministic machines by using a small amount of space.

For time complexity, such a simulation seems to require an exponential increase in time. For space complexity, this theorem shows that any non-deterministic Turing machine that uses ** f(n)** space can be converted to a deterministic TM that uses

Hence, Savitch’s theorem states that, for any function, $f \colon N \rightarrow R^+$, where $f(n) \geqslant n$

*NSPACE(f(n)) ⊆ SPACE(f(n))*

The following diagram depicts the relationship among different complexity classes.

Till now, we have not discussed P and NP classes in this tutorial. These will be discussed later.

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