When planning a trip, we need to consider the time and cost required to travel between several cities. Let’s solve the problem of creating an optimal travel plan based on the information provided below.<\/p>\n
You are given a graph representing N cities and the travel time\/cost between each pair of cities. Write a program to calculate the optimal route that starts from the initial city, visits all cities in minimum time or cost, and returns to the starting city.<\/p>\n
Print the minimum travel time (or cost).<\/p>\n
Output:<\/strong> This problem involves finding a path that minimizes the travel time between the given cities. This is commonly known as the Traveling Salesman Problem (TSP)<\/strong>, which is an NP-hard problem. The input graph is represented as an adjacency matrix, and we need to explore the optimal route based on this matrix.<\/p>\n To solve this problem, we will combine Depth-First Search (DFS) with a Dynamic Programming (DP) technique using bit masking. Bit masking allows us to efficiently represent the state of visited cities and helps avoid redundant calculations.<\/p>\n The above code shows the overall flow of the algorithm for optimizing the travel route. The main parts are as follows:<\/p>\n
\n 75\n <\/div>\nProblem Solving Process<\/h2>\n
1. Understanding the Problem<\/h3>\n
2. Approach<\/h3>\n
3. Kotlin Implementation<\/h3>\n
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\n fun findMinCost(graph: Array4. Code Explanation<\/h3>\n
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5. Performance Analysis<\/h3>\n