algorithm for estimating traveling salesman problem in matlab

To estimate the traveling salesman problem using the dynamic programming technique in Matlab, we can follow the steps below:

  1. Define the input parameters for the algorithm: the distance matrix dist between cities and the starting city start.
  2. Compute the number of cities n and initialize the dp table with Inf values and set dp(start, 1) = 0.
  3. For j from 2 to n, and k from 1 to n-1:
    • For each S subset of cities of size j that contains start and k, compute dp(S,j) as:
      main.m
      dp(S,j) = min(dp(S-{k}, j-1) + dist(k, m))
      
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      where m is any city in S.
  4. Finally, the optimized distance optDist is given by:
    main.m
    optDist = min(dp({1,2,...,n}, n) + dist(k, start))
    
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    where k is any city that leads to the optimal tour.

Here's the Matlab code implementing this algorithm:

main.m
function optDist = tsp_dp(dist, start)
% Inputs:
%    dist: distance matrix between cities
%    start: starting city
% Output:
%    optDist: optimized distance for the TSP

n = size(dist, 1);
dp = Inf(n, n);

% Initialize dp table
dp(start, 1) = 0;

% Compute dynamic programming table
for j = 2:n
    for k = 1:n-1
        if k == start
            continue;
        end
        
        for S = combnk([1:n], j)
            if ~ismember(start, S) || ~ismember(k, S)
                continue;
            end
            
            for m = S
                if m == k
                    continue;
                end
                
                dp(S, j) = min(dp(setdiff(S, k), j-1) + dist(k, m));
            end
        end
    end
end

% Compute optimized distance
optDist = min(dp(setdiff([1:n], start), n) + dist(k, start));
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