[Problem] ring ring problem solving template

Generally refers to the ring problem has two solutions, the ultimate answer is to return two of the best. Common are:

• Angle clock
• The distance between the bus station

The general problem-solving ideas are similar, from these points not wrong:

• First, find the total length of the sum.
• After the solution was then determined visually sub.
• Finally, by comparing sum - subthe subselected answer merits.

...
int sum = ?
int sub = ?
return chioce(sub-sub, sub);
...

Clock angle C_01

Method a: Method of complementary

Complexity Analysis

• time complexity: $O()$ ,
• Space complexity: $O()$ ,

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The distance between the bus station C_02

With n stations on the ring bus routes, in sequence from 0 to n - 1 is numbered. We know the distance between each pair of adjacent bus stop, distance [i] represents the number of stations and the number i is the distance between the (i + 1)% n stations.

Bus on the ring can travel in the direction of clockwise and counterclockwise.

Passengers return to the starting point to start from the shortest distance between destination destination.

输入：distance = [1,2,3,4], start = 0, destination = 3
输出：4
解释：公交站 0 和 3 之间的距离是 6 或 4，最小值是 4。

Method a: Method of complementary

And the angle clock seeking nature to a class of questions. Because it is a circular area inside the shortest path, so we should be a little "opportunistic" look,

• We first find the total length of the sum, and obtaining the distance d s to dis.
• Finally, compare the sum-dis dis size can be.

public int distanceBetweenBusStops(int[] distance, int start, int destination) {
  int s = Math.min(start, destination);
  int d = Math.max(start, destination);
  
  int sum = 0;
  for (int dis : distance)     
  	sum += dis;

  int dis = 0;
  for (int i = s; i < d; i++)  
  	dis += distance[i];
  
  return Math.min(sum-dis, dis);
}

Complexity Analysis

• time complexity: $O (n)$ ,
• Space complexity: $O (1)$ ,

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A_04

method one:

Complexity Analysis

• time complexity: $O()$ ,
• Space complexity: $O()$ ,

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