A step-by-step process that solves a problem in a set amount of time is called an algorithm. It is a clearly defined series of commands that accepts inputs, acts on the inputs, and outputs a result. Algorithms are used to carry out a variety of tasks, from straightforward arithmetic operations to intricate data analysis and machine learning. They can be implemented in hardware or software. An algorithm's effectiveness and precision are crucial components in its design and execution.
Algorithm techniques refer to various methods, approaches, and strategies used to design and analyze algorithms. These techniques are used to solve computational problems in various domains, such as computer science, mathematics, operations research, and engineering.
Here are some common algorithmic techniques used in computer science:
Sorting is the process of arranging data elements in a specific order, such as ascending or descending order. Here are some of the most common sorting algorithms:
Bubble Sort: A simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order.
Insertion Sort: A simple sorting algorithm that builds the final sorted array one item at a time, by inserting each element in its proper place.
Selection Sort: A simple sorting algorithm that selects the minimum element from the unsorted part of the array and swaps it with the first element.
Merge Sort: A divide and conquer sorting algorithm that recursively divides the unsorted list into n sublists, sorts each sublist, and then merges them into a single, sorted list.
Quick Sort: A divide and conquer sorting algorithm that selects a pivot element and partitions the array around it, such that all elements smaller than the pivot are to the left and all elements greater than the pivot are to the right.
Heap Sort: A comparison-based sorting algorithm that creates a binary heap data structure and repeatedly extracts the maximum element from the heap until it is empty.
Radix Sort: A non-comparison based sorting algorithm that sorts data elements based on their digits or radices.
Sorting algorithms can have different time and space complexities, and the choice of algorithm depends on the specific requirements of the problem. It is important to have a deep understanding of the various sorting algorithms and their trade-offs to be able to choose the right algorithm for a given problem.
Searching is the process of finding a specific item or data element in a collection of data. Here are some common searching algorithms:
Linear Search: A simple search algorithm that checks each element of the list in sequence until the desired item is found.
Binary Search: A search algorithm that uses a divide and conquer approach to search for an element in a sorted array. It repeatedly divides the search interval in half until the target value is found or it is clear that the target is not in the array.
Jump Search: A search algorithm that works by dividing the list into blocks of fixed size and then performing a linear search in each block.
Interpolation Search: A search algorithm that uses an estimate of the position of the target value based on the values of the first and last elements of the list.
Exponential Search: A search algorithm that works by dividing the list into blocks of size two and then performing a binary search in each block.
Ternary Search: A search algorithm that uses a divide and conquer approach to search for an element in an array, dividing the list into three parts rather than two.
The choice of search algorithm depends on the specific requirements of the problem, such as the size of the data collection, the type of data, and whether the data is sorted or not. It is important to have a deep understanding of the various searching algorithms and their trade-offs to be able to choose the right algorithm for a given problem.
💾 Dynamic Programming
Dynamic Programming is a optimization technique used to solve problems by breaking them down into smaller, overlapping subproblems and using the solutions to these subproblems to build up the solution to the original problem. It is particularly useful for solving problems with optimal substructure, where the optimal solution to a problem can be expressed in terms of optimal solutions to smaller subproblems.
Dynamic programming involves two steps:
Characterize the structure of an optimal solution.
Recursively compute the value of an optimal solution, typically in a bottom-up fashion.
Dynamic programming can be applied to a wide range of problems, including:
Combinatorial optimization problems such as the traveling salesman problem and knapsack problem.
Sequence problems such as longest common subsequence and edit distance.
Resource allocation problems such as the matrix chain multiplication problem.
Dynamic programming can also be used in conjunction with other algorithms, such as divide-and-conquer, to achieve even better results. It is important to have a deep understanding of the dynamic programming technique, including the underlying principles and the different approaches, to be able to effectively apply it to real-world problems.
💻 Greedy Algorithms
Greedy Algorithms is a algorithmic paradigm that makes the locally optimal choice at each stage with the hope of finding a global optimum. In a greedy algorithm, the solution to a problem is constructed step by step and at each step, the best available option is chosen, with the hope that the local optimal choices will lead to a global optimal solution.
Here are some of the key features of greedy algorithms:
Greedy algorithms are easy to understand and implement.
They are fast and efficient, especially for large-scale problems.
The choice made at each step is based on the available information and does not consider the future impact of the choice.
Greedy algorithms are used to solve optimization problems, where the goal is to find the best solution among all possible solutions.
The choice made at each step is irreversible, so if a local optimal choice leads to a suboptimal solution, it cannot be corrected later.
Examples of problems that can be solved using greedy algorithms include:
Activity selection problem.
Fractional knapsack problem.
Minimum spanning tree problem.
It is important to have a good understanding of the problem, the constraints, and the possible solutions before applying a greedy algorithm. This will help ensure that the algorithm is applied correctly and that the solution found is indeed optimal.
➗ Divide and Conquer
Divide and Conquer is a algorithmic paradigm that involves dividing a problem into smaller subproblems and solving each subproblem individually, before combining the solutions to these subproblems to obtain a solution to the original problem. This approach can reduce the time complexity of the problem, as well as make it easier to solve.
Here are some of the key features of divide and conquer algorithms:
Divide and conquer algorithms break down complex problems into smaller, more manageable subproblems.
They are particularly useful for problems with a recursive structure, where the solution to a problem can be expressed in terms of solutions to smaller subproblems.
Divide and conquer algorithms often lead to efficient algorithms, especially for problems with large input sizes.
They can be used to solve a wide range of problems, including sorting, searching, and computational geometry problems.
Divide and conquer algorithms can be combined with other algorithmic paradigms, such as dynamic programming, to achieve even better results.
Examples of problems that can be solved using divide and conquer algorithms include:
Closest pair of points
Strassen's matrix multiplication
It is important to have a deep understanding of the divide and conquer technique, including the underlying principles and the different approaches, to be able to effectively apply it to real-world problems.
Backtracking is a general algorithmic technique for finding all (or some) solutions to a computational problem by incrementally constructing solutions and abandoning a solution as soon as it is determined to be not feasible. It is used for problems where finding a solution is equivalent to finding a path through a tree-like structure.
Here are some of the key features of backtracking algorithms:
Backtracking algorithms incrementally build solutions by making a series of choices, each of which may be partially correct or incorrect.
The algorithm continually checks if the solution is feasible, and if not, it undoes the previous choice and tries a different option.
The algorithm is a systematic method for generating all possible solutions to a problem and testing them for feasibility.
Backtracking algorithms can be used to solve problems in fields such as combinatorial optimization, constraint satisfaction, and recursive search.
The efficiency of backtracking algorithms is dependent on the order in which choices are made and the criteria for determining feasibility.
Examples of problems that can be solved using backtracking algorithms include:
The N-Queens problem
The m-coloring problem
Generating all subsets of a set
The knapsack problem
Finding all Hamiltonian cycles in a graph
It is important to have a good understanding of the problem, the constraints, and the possible solutions before applying a backtracking algorithm. This will help ensure that the algorithm is applied correctly and that the solution found is indeed optimal.
🌿 Branch and Bound
Branch and Bound is a general algorithmic technique for solving optimization problems by systematically exploring all feasible solutions. It involves dividing the search space into smaller subspaces, or branches, and then using bounds on the solution space to eliminate subspaces that cannot contain an optimal solution.
Here are some of the key features of branch and bound algorithms:
Branch and bound algorithms use a tree-like structure to explore the solution space, where each node in the tree corresponds to a partially specified solution.
The algorithm starts with an initial bound on the optimal solution and then refines this bound by considering the subspaces, or branches, defined by the partial solutions.
The algorithm eliminates subspaces that cannot contain an optimal solution, reducing the search space and improving the efficiency of the algorithm.
Branch and bound algorithms are particularly useful for solving combinatorial optimization problems, where the solution space is too large to be exhaustively searched.
The efficiency of branch and bound algorithms depends on the quality of the bounds and the order in which the subspaces are explored.
Examples of problems that can be solved using branch and bound algorithms include:
The traveling salesman problem
The knapsack problem
The maximum clique problem
The integer programming problem
The set-covering problem
It is important to have a good understanding of the problem, the constraints, and the possible solutions before applying a branch and bound algorithm. This will help ensure that the algorithm is applied correctly and that the solution found is indeed optimal.
🔀 Randomized Algorithms
Randomized algorithms are algorithms that use randomness as a key component in their design and execution. These algorithms can solve computational problems by making random choices, or by generating random inputs, and they are often used when traditional deterministic algorithms are too slow or when there is no deterministic algorithm known to solve the problem.
Here are some of the key features of randomized algorithms:
Randomized algorithms use random numbers, random sampling, or random selection to make decisions or guide their progress.
Randomized algorithms can be faster than deterministic algorithms for some problems, especially when the problem size is very large.
The output of randomized algorithms is often probabilistic, and the algorithm may produce different outputs for different inputs.
Randomized algorithms are often used for problems that are too difficult to solve using deterministic algorithms, such as optimization problems or problems in graph theory.
The performance of randomized algorithms can be analyzed using probability theory and the study of random processes.
Examples of problems that can be solved using randomized algorithms include:
Randomized algorithms for graph problems, such as finding a minimum spanning tree or a maximum flow
Randomized algorithms for computational geometry, such as finding the convex hull of a set of points
Monte Carlo methods for solving optimization problems or estimating values in large-scale simulations
Randomized algorithms for cryptography and secure communication.
It is important to understand the underlying theory and the limitations of randomized algorithms, as well as the potential sources of randomness and the methods for generating random numbers, in order to effectively apply randomized algorithms to solve computational problems.
Problem definition: Make sure the algorithm is well-suited to solving the problem it was designed for and that the problem is clearly defined.
Input/Output specifications: Check that the inputs and outputs are clearly specified, and that the algorithm handles all edge cases and exceptional situations.
Algorithm design: Evaluate the design of the algorithm, including its structure, logic, and complexity. Consider the use of data structures and algorithms that are appropriate for the problem.
Correctness: Test the algorithm with a variety of inputs and verify that it produces the correct outputs.
Time and space complexity: Analyze the time and space complexity of the algorithm, and assess its efficiency and scalability.
Code implementation: Check the implementation of the algorithm in code, including readability, maintainability, and adherence to coding standards.
Test cases: Verify that the algorithm has been thoroughly tested and that the test cases cover all possible scenarios.
These are some of the key areas to focus on when reviewing an algorithm. The goal is to ensure that the algorithm is correct, efficient, and easy to maintain and understand.
❓ Sample Problem
The storage capacity of hard drives dwarfs that of RAM. This can lead to interesting space-time trade-offs.
Suppose you were given a file containing roughly one billion IP addresses, each of which is a 32-bit quantity. How would you programmatically find an IP address that is not in the file? Assume you have unlimited drive space but only a few megabytes of RAM at your disposal.
💡 Hint: Can you be sure there is an address which is not in the file?
Since the file can be treated as consisting of 32-bit integers, we can sort the input file and then iterate through it, searching for a gap between values. The time complexity is 0(n log n),where n is number of entries. Furthermore, to keep the RAM usage low, the sort will have to use disk as storage, which in practice is very slow
Note that we cannot just compute the largest entry and add one to it, since if the largest entry is 255.255.255.255 (the highest possible IP address), adding one to it leads to overflow. The same holds for the smallest entry. (In practice this would be a good heuristic.)
We could add all the IP addresses in the file to a hash table, and then enumerate IP addresses, starting with 0.0.0.0, until we find one not in the hash table. However, a hash table requires considerable overhead—of the order of 10 bytes for each integer, i.e., of the order of 10 gigabytes.
We can reduce the storage requirement by an order of magnitude by using a bit array representation for the set of all possible IP addresses. Specifically, we allocate an array of 2^32 bits, initialized to 0, and write a 1 at each index that corresponds to an IP address in the file. Then we iterate through the bit array, looking for an entry set to 0. There are 2^32 * 4 X 10^9 possible IP addresses, so not all IP addresses appear in the file. The storage is 2^32/8 bytes, is half a gigabyte. This is still well in excess of the storage limit.
Since the input is in a file, we can make multiple passes through it. We can use this to narrow the search down to subsets of the space of all IP addresses as follows. We make a pass through the file to count the number of IP addresses present whose leading bit is a 1, and the number of IP addresses whose leading bit is a 0. At least one IP address must exist which is not present in the file, so at least one of these two counts is below 2^31. For example, suppose we have determined using counting that there must be an IP address which begins with 0 and is absent from the file. We can focus our attention on IP addresses in the file that begin with 0, and continue the process of elimination based on the second bit. This entails 32 passes, and uses only two integer-valued count variables as storage.
Since we have more storage, we can count on groups of bits. Specifically, we can count the number of IP addresses in the file that begin with 0,1,2, . . . , 2^16 -1 using an array of 2^16 32-bit integers. For every IP address in the file, we take its 16 MSBs to index into this array and increment the count of that number. Since the file contains fewer than 232 numbers, there must be one entry in the array that is less than 2^16. This tells us that there is at least one IP address which has those upper bits and is not in the file. In the second pass, we can focus only on the addresses whose leading 16 bits match the one we have found, and use a bit array of size 2^16 to identify a missing address