# Merge Overlapping Intervals

Interval merging is a well-known challenge in computer science and mathematics. It revolves around combining a set of intervals and merging those that overlap to yield a brief list of non-overlapping intervals. This particular problem finds relevance in various disciplines, encompassing scheduling, data analysis, and computational geometry. In the subsequent sections, we will delve into the concept, the algorithmic approach, and the practical applications of this procedure.

### Understanding Overlapping Intervals

Before we immerse ourselves in the intricacies of algorithmic solutions, it's crucial to establish a clear comprehension of what is meant by "overlapping intervals." Typically, an interval is denoted as [start, end], with 'start' representing the interval's initiation point and 'end' designating its termination point. Intervals are considered to overlap when the endpoint of one interval is greater than or equal to the starting point of another interval.

### Example

Intervals: [1, 3], [2, 6], [8, 10], [15, 18]

[1, 3] and [2, 6] overlap in this set of intervals because the finishing point of the first interval (3) is larger than or equal to the starting point of the second interval (2).

### Algorithm

The following algorithm can be used to combine overlapping intervals:

1. Sort the intervals in non-descending order depending on their start positions. This step ensures that intervals with earlier start times are prioritized.
2. Create an empty list to hold the combined intervals.
3. Iterate over the sorted intervals one by one, beginning with the second (index 1).
1. Compare the current interval's start point (interval[i]) to the endpoint of the previous interval in the merged list (merged_intervals[-1]).
2. Add interval[i] to the merged list if it does not overlap with the final interval in the merged list (interval[i][0] > merged_intervals[-1][1]).
3. If interval[i] overlaps with the last interval in the merged list (interval[i][0] = merged_intervals[-1][1]), merge the intervals by making the endpoint of the last interval in the merged list the maximum of both intervals' endpoints (merged_intervals[-1][1] = max(merged_intervals[-1][1], interval[i][1]).

### Key objectives

Efficiency: Consolidate overlapping periods efficiently to prevent redundancy and reduce scheduling complexity.

Non-Overlapping Intervals: Check that the resultant interval list is not overlapping. It ensures that every time slot is either open for scheduling or is taken by a single meeting.

Optimization: Resource allocation may be optimized by finding common time slots for combining overlapping meetings, resulting in more effective utilization of available resources.

Analysis of Complexity

While the Brute Force method is theoretically straightforward, it has a temporal complexity of O(n2), where 'n' is the number of intervals. This is due to the fact that each interval must be compared to every other interval, resulting in a quadratic time complexity. As a result, for huge datasets, this method might need to be more active and efficient.

### Approach 1: Brute Force

A brute force technique is one basic way. It entails comparing each interval in the list to every other interval to see if they overlap. When two intervals intersect, they are combined. This method could be more efficient since its temporal complexity is O(n2), where n is the number of intervals.

Implementation

Output:

```[1, 6], [8, 10] [15, 18]
```

In this example, the intervals [1, 3] and [2, 6] overlap; therefore they are combined into a single interval [1, 6]. The non-overlapping intervals [8, 10] and [15, 18] remain intact.

### Approach 2: Sort and Merge

A more efficient method is arranging the intervals according to their starting places. Once the list has been sorted, you may cycle over it, merging overlapping intervals as you come across them. Because of the sorting phase, the temporal complexity of this solution is O(n log n), where n is the number of intervals.

Implementation

Output:

```[1, 6] [8, 10] [15, 18]
```

The intervals [1, 3] and [2, 6] in this example overlap and are successfully merged into a single interval [1, 6]. The non-overlapping intervals [8, 10] and [15, 18] stay the same. The code displays the merged intervals to the console, enclosing each interval in square brackets and separating them by spaces.

### Approach 3: Interval Tree

An interval tree is a data structure built primarily for interval-related problems. It enables efficient interval addition, deletion, and querying. Building an interval tree using the specified intervals and then querying for overlapping intervals can result in a solution with an O(n log n) time complexity.

Implementation

Output:

```[15, 20] overlaps with [14, 16]
[10, 30] overlaps with [14, 16]
[17, 19] overlaps with [14, 16]
[5, 20] overlaps with [14, 16]
[12, 15] overlaps with [14, 16]
```

The algorithm correctly detects and publishes the intervals overlapping with the query interval from the interval tree [14, 16]. Each line in this output represents each interval from the tree that overlaps with the query interval, with the start and end points of both the overlapping interval and the query interval shown.

### Approach 4: Dynamic Programming

The interval merging problem can alternatively be solved using dynamic programming. As you go over the sorted list of intervals, you may use a dynamic programming table to keep track of merging intervals and their endpoints. This method has an O(n) time complexity and is efficient for big datasets.

Implementation

Output:

```[1, 6] [8, 10] [15, 18]
```

The intervals [1, 3] and [2, 6] in this example overlap and are combined into a single interval [1, 6]. The non-overlapping intervals [8, 10] and [15, 18] are unaffected. The combined intervals are successfully printed to the console, with each interval contained in square brackets and separated by spaces.

### Applications:

Interval merging has practical applications in a variety of fields, including:

Scheduling: Tasks or events with overlapping periods may exist in project management and resource allocation. Combining these periods can aid in scheduling and resource use.

Data Analysis: When working with time-based data, such as stock prices or sensor measurements, combining overlapping intervals can result in more concise and useful visualizations.

Database Management: When working with time series data or event logs, interval merging is important in databases. It can assist in enhancing query performance and make data retrieval easier.

Computational Geometry: Interval merging is used in segment intersection and other geometric methods in computational geometry. When working with line segments on a plane, for example, combining overlapping segments streamlines further processing.

Meeting Scheduling: Merging overlapping time slots in calendar apps or scheduling software makes it easier to discover open meeting times.