Sorting algorithms arrange data efficiently. Common ones include bubble sort, insertion sort, selection sort, and merge sort.
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Dynamic programming breaks down problems into smaller parts & uses previous results to find sols. It employs top-down & bottom-up approaches.
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Search algorithms locate elements in data structures. Linear and binary search are widely used methods.
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Tree traversal algorithms visit each node in trees. There are three types: pre-order, in-order, and post-order traversals.
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Hashing algorithms convert data into fixed-sized values. They are essential in blockchain and maintain data integrity.
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String searching algorithms find patterns in text. They have applications in spam filters, plagiarism detection, and more.
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Divide and conquer algorithms break problems into subproblems and combine solutions. Quick and merge sorts are examples.
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Backtracking explores all possible solutions incrementally. It reverts when no solution is found and is used in various problem-solving scenarios.
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Randomised algorithms use randomness for decision-making. They reduce complexity and memory usage in programs.
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Brute force explores all possible choices to find solutions. While simple, it can be slow for large datasets.
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