- How do you solve TSP?
- What does the algorithmic analysis count?
- What are the drawbacks of dynamic programming?
- Is NP-hard harder than NP-complete?
- Why is TSP NP hard?
- What is the time complexity of TSP?
- Why travel salesman problem is NP-complete?
- Is Hamiltonian path NP-complete?
- What is the main objective of TSP?
- Is TSP a NP?
- What happens when the backtracking algorithm reaches a complete solution?
- Has anyone solved the traveling salesman problem?
- What is the complexity of the following loop for I 0 I?
- What is TSP in AI?
- What is NP problem example?
- How many subproblems are in TSP at most?
- How many times each city should be visited in the Travelling salesman problem?
- What is the Travelling salesman problem equivalent to in graph theory?
- Which problem is not NP-complete?
- Is vertex cover NP-complete?

## How do you solve TSP?

To solve the TSP using the Brute-Force approach, you must calculate the total number of routes and then draw and list all the possible routes.

Calculate the distance of each route and then choose the shortest one—this is the optimal solution.

This method breaks a problem to be solved into several sub-problems..

## What does the algorithmic analysis count?

Answer. Explanation: In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms – the amount of time, storage, or other resources needed to execute them. … These estimates provide an insight into reasonable directions of search for efficient algorithms.

## What are the drawbacks of dynamic programming?

Disadvantages of Dynamic Programming over recursionIt takes a lot of memory to store the calculated result of every subproblem without ensuring if the stored value will be utilized or not.Many times, output value gets stored and never gets utilized in the next subproblems while execution.More items…

## Is NP-hard harder than NP-complete?

An NP-hard problem can be beyond NP. The polynomial-time reduction from your X to any problem in NP does not necessarily have a polynomial-time inverse. If the inverse is harder, then the verification is harder. An NP-complete problem, on the other hand, is one that is NP-hard and itself in NP.

## Why is TSP NP hard?

Why is TSP not NP-complete? … Since it takes exponential time to solve NP, the solution cannot be checked in polynomial time. Thus this problem is NP-hard, but not in NP. In general, for a problem to be NP-complete it has to be a “decision problem”, meaning that the problem is to decide if something is true or not.

## What is the time complexity of TSP?

There are at most O(n*2n) subproblems, and each one takes linear time to solve. The total running time is therefore O(n2*2n). The time complexity is much less than O(n!), but still exponential. Space required is also exponential.

## Why travel salesman problem is NP-complete?

Thus we can say that the graph G’ contains a TSP if graph G contains Hamiltonian Cycle. Therefore, any instance of the Travelling salesman problem can be reduced to an instance of the hamiltonian cycle problem. Thus, the TSP is NP-Hard.

## Is Hamiltonian path NP-complete?

The problem is to determine if there is a simple path that crosses each vertex of the graph. A Hamiltonian path is a simple open path that contains each vertex in a graph exactly once. … Hamiltonian Cycle is NP-complete, so we may try to reduce this problem to Hamiltonian Path.

## What is the main objective of TSP?

The salesman’s goal is to keep both the travel costs and the distance traveled as low as possible. Focused on optimization, TSP is often used in computer science to find the most efficient route for data to travel between various nodes. Applications include identifying network or hardware optimization methods.

## Is TSP a NP?

Traveling Salesman Optimization(TSP-OPT) is a NP-hard problem and Traveling Salesman Search(TSP) is NP-complete. However, TSP-OPT can be reduced to TSP since if TSP can be solved in polynomial time, then so can TSP-OPT(1).

## What happens when the backtracking algorithm reaches a complete solution?

3. What happens when the backtracking algorithm reaches a complete solution? Explanation: When we reach a final solution using a backtracking algorithm, we either stop or continue searching for other possible solutions.

## Has anyone solved the traveling salesman problem?

After a lengthy analysis, the Stanford-McGill team was finally able to beat out Christofides’ algorithm by a tiny margin for “graphical” traveling salesman problems, a wide subclass.

## What is the complexity of the following loop for I 0 I?

The time complexity of a loop is equal to the number of times the innermost statement is to be executed. On the first iteration of i=0, the inner loop executes 0 times.

## What is TSP in AI?

ABSTRACT. The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics and combinatorial optimization. … As results compared with the exactly optimal solutions, the AI search techniques can provide very satisfactory solutions for all TSP problems.

## What is NP problem example?

Examples. An example of an NP-hard problem is the decision subset sum problem: given a set of integers, does any non-empty subset of them add up to zero? That is a decision problem and happens to be NP-complete.

## How many subproblems are in TSP at most?

There are at the most 2n. n sub-problems and each one takes linear time to solve. Therefore, the total running time is O(2n.

## How many times each city should be visited in the Travelling salesman problem?

We can say that salesman wishes to make a tour or Hamiltonian cycle, visiting each city exactly once and finishing at the city he starts from. There is a non-negative cost c (i, j) to travel from the city i to city j. The goal is to find a tour of minimum cost. We assume that every two cities are connected.

## What is the Travelling salesman problem equivalent to in graph theory?

As a graph problem TSP can be modelled as an undirected weighted graph, such that cities are the graph’s vertices, paths are the graph’s edges, and a path’s distance is the edge’s weight. It is a minimization problem starting and finishing at a specified vertex after having visited each other vertex exactly once.

## Which problem is not NP-complete?

So an example of a problem in NP but not NP-Complete is the sorting problem. i.e. Given integers, rearrange the numbers such that they are in non-decreasing order. This can be easily solved in (well, actually better). Clearly, you can verify if a proposed solution is actually a solution in , which is polynomial in .

## Is vertex cover NP-complete?

Its decision version, the vertex cover problem, was one of Karp’s 21 NP-complete problems and is therefore a classical NP-complete problem in computational complexity theory.