Binary search works by repeatedly dividing the search interval in half. This requires knowing whether the target value is in the lower or upper half, which is only possible if the list is sorted.

Divide the list into smaller sublists, recursively sort the sublists, and then merge them back together in sorted order.

O(n) in the worst case, where n is the number of elements in the list.

O(log n), where n is the number of elements in the list.

O(n^2) in the worst and average cases, where n is the number of elements in the list.

O(n^2) in all cases, where n is the number of elements in the list.

O(n log n) in all cases, where n is the number of elements in the list.

Merge Sort requires more memory due to the creation of temporary arrays during the merge process.

Recursion can make code more concise and easier to read for certain problems.

Recursion can lead to stack overflow errors if the base case is not reached or the recursion depth is too large.

A search algorithm that finds the position of a target value within a sorted array by repeatedly dividing the search interval in half.

A divide-and-conquer sorting algorithm that divides the input array into smaller sub-arrays, recursively sorts them, and then merges the sorted sub-arrays.

A programming technique where a function calls itself in order to solve a problem.

The condition that stops a recursive function from calling itself infinitely.

A searching algorithm that sequentially checks each element of a list until a match is found or the entire list has been searched.

A sorting algorithm that builds the final sorted array one item at a time by repeatedly inserting the next element into its correct position within the already sorted portion of the array.

A sorting algorithm that repeatedly finds the minimum element from the unsorted portion of the array and places it at the beginning.

A resizable array implementation of the List interface in Java.

The position of an element within an array or ArrayList, starting from 0.

A contiguous portion of a list or array.

Linear Search: Works on unsorted lists, O(n) time complexity. Binary Search: Requires sorted lists, O(log n) time complexity.

Insertion Sort: Builds sorted array incrementally, adaptive. Selection Sort: Finds the minimum element in each iteration, not adaptive.

Insertion Sort: Simple, in-place, O(n^2) time complexity. Merge Sort: More complex, requires extra space, O(n log n) time complexity.

Selection Sort: Simple, in-place, O(n^2) time complexity. Merge Sort: More complex, requires extra space, O(n log n) time complexity.

Iterative: Uses loops, generally more efficient in terms of memory. Recursive: Uses function calls, can be more concise, but may lead to stack overflow.

Iterative: Uses loops, generally more efficient in terms of memory. Recursive: Uses function calls, can be more concise, but may lead to stack overflow.

Iterative: Uses loops, generally more efficient in terms of memory. Recursive: Uses function calls, can be more concise, but may lead to stack overflow.

Iterative: Uses loops, generally more efficient in terms of memory. Recursive: Uses function calls, can be more concise, but may lead to stack overflow.

Iterative: Uses loops, generally more efficient in terms of memory. Recursive: Uses function calls, can be more concise, but may lead to stack overflow.

Merge Sort is preferred for larger datasets due to its O(n log n) time complexity, while Insertion Sort is better for small datasets or nearly sorted data.