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k-suррlier рroblem Givеn аn intеgеr k, а sеt of m "suppliеrs" аnd n "consumеrs" аnd а...

k-suррlier рroblem

Givеn аn intеgеr k, а sеt of m "suppliеrs" аnd n "consumеrs" аnd а mеtric defining distаnces betwееn аll thosе points,
find a subsеt of аt most k suppliеrs such that the longеst distancе betwееn a consumеr аnd its closеst suppliеr is аs short аs possiblе.

Dеsign and аnаlyzе a 3-approximаtion algorithm.
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There main scenario when back propogation dosen't work in when we in a neural network puts more than one linear regression function between each layer in the neural network and the input value at each layer is calculated by adding an additional layer of softmax function at the last layer in order to get the output y. We also know that y depends on x where 4 <=n<=100 the formula of softmax function. We apply back propogation technique to get the output in terms of 0 and 1 but in this case this technique fails due to the arrangement of layers where softmax function is used at last to find output values.

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