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Are there any efficient algorithms to solve longest path problem in networks with cycles?


When are Decision Diagrams the right way to model and solve a problem?Combinatorial Optimization: Metaheuristics, CP, IP — “versus” or “and”?Relationship between the Assignment Problem and the Stable Marriage ProblemAlgorithmic gap for Hochbaum's (greedy) algorithm for (metric) uncapacitated facility location













4












$begingroup$


I have a directed social network and as a preprocessing step i need to calculate longest path lengths for each node. Longest path problem is np hard as far as i know but i've seen dynamic programming methods for DAGs. Is there such a method for general networks with cycles. All arc weights are one.









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    $begingroup$


    I have a directed social network and as a preprocessing step i need to calculate longest path lengths for each node. Longest path problem is np hard as far as i know but i've seen dynamic programming methods for DAGs. Is there such a method for general networks with cycles. All arc weights are one.









    share







    New contributor



    Evren Guney is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.






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      4












      4








      4





      $begingroup$


      I have a directed social network and as a preprocessing step i need to calculate longest path lengths for each node. Longest path problem is np hard as far as i know but i've seen dynamic programming methods for DAGs. Is there such a method for general networks with cycles. All arc weights are one.









      share







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      Evren Guney is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






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      I have a directed social network and as a preprocessing step i need to calculate longest path lengths for each node. Longest path problem is np hard as far as i know but i've seen dynamic programming methods for DAGs. Is there such a method for general networks with cycles. All arc weights are one.







      combinatorial-optimization





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      asked 8 hours ago









      Evren GuneyEvren Guney

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          2 Answers
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          $begingroup$

          There is no theoretically efficient method, unless P=NP.



          The Hamiltonian Path Problem is the problem of determining whether there exists a path in an undirected or directed graph that visits each vertex exactly once. This problem is NP-complete (see link).



          If you could determine the longest path efficiently, you could do so for every starting point and ending point. If for any pair the length is equal to the number of points minus one, you have proven that there exists an Hamiltonian path. If not, then there is no Hamiltonian path.



          It follows that determining the longest path must be NP-hard.






          share|improve this answer








          New contributor



          Kevin Dalmeijer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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            2












            $begingroup$

            As observed by Kevin Dalmeijer, you cannot expect an efficient method unless $P=NP$.



            Since you're asking explicitly for dynamic programming: define $C(s,t,V)$ as the longest path from $s$ to $t$ without visiting the vertices in $V$. Values $C$ satisfy
            beginalign*
            C(s,t,V)=
            begincases
            max_uin N^-(t)setminus V C(s,u,Vcupt)+d_ut, & textif $sneq t$ and $N^-(t)setminus Vneq emptyset$,\
            -infty, & textif $sneq t$ and $N^-(t)setminus V=emptyset$,\
            0, & textif $s=t$,
            endcases
            endalign*

            where $N^-(t)$ is the set of predecessors of vertex $t$, and $d_uv$ is the distance between $u$ and $v$. Computing $C$ for fixed $s$ takes time $O(n^22^n)$ and space $O(n2^n)$ and $C(s,t,emptyset)$ gives the longest path between $s$ and $t$.






            share|improve this answer









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              2 Answers
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              2 Answers
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              active

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              6












              $begingroup$

              There is no theoretically efficient method, unless P=NP.



              The Hamiltonian Path Problem is the problem of determining whether there exists a path in an undirected or directed graph that visits each vertex exactly once. This problem is NP-complete (see link).



              If you could determine the longest path efficiently, you could do so for every starting point and ending point. If for any pair the length is equal to the number of points minus one, you have proven that there exists an Hamiltonian path. If not, then there is no Hamiltonian path.



              It follows that determining the longest path must be NP-hard.






              share|improve this answer








              New contributor



              Kevin Dalmeijer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.





              $endgroup$

















                6












                $begingroup$

                There is no theoretically efficient method, unless P=NP.



                The Hamiltonian Path Problem is the problem of determining whether there exists a path in an undirected or directed graph that visits each vertex exactly once. This problem is NP-complete (see link).



                If you could determine the longest path efficiently, you could do so for every starting point and ending point. If for any pair the length is equal to the number of points minus one, you have proven that there exists an Hamiltonian path. If not, then there is no Hamiltonian path.



                It follows that determining the longest path must be NP-hard.






                share|improve this answer








                New contributor



                Kevin Dalmeijer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.





                $endgroup$















                  6












                  6








                  6





                  $begingroup$

                  There is no theoretically efficient method, unless P=NP.



                  The Hamiltonian Path Problem is the problem of determining whether there exists a path in an undirected or directed graph that visits each vertex exactly once. This problem is NP-complete (see link).



                  If you could determine the longest path efficiently, you could do so for every starting point and ending point. If for any pair the length is equal to the number of points minus one, you have proven that there exists an Hamiltonian path. If not, then there is no Hamiltonian path.



                  It follows that determining the longest path must be NP-hard.






                  share|improve this answer








                  New contributor



                  Kevin Dalmeijer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                  Check out our Code of Conduct.





                  $endgroup$



                  There is no theoretically efficient method, unless P=NP.



                  The Hamiltonian Path Problem is the problem of determining whether there exists a path in an undirected or directed graph that visits each vertex exactly once. This problem is NP-complete (see link).



                  If you could determine the longest path efficiently, you could do so for every starting point and ending point. If for any pair the length is equal to the number of points minus one, you have proven that there exists an Hamiltonian path. If not, then there is no Hamiltonian path.



                  It follows that determining the longest path must be NP-hard.







                  share|improve this answer








                  New contributor



                  Kevin Dalmeijer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                  Check out our Code of Conduct.








                  share|improve this answer



                  share|improve this answer






                  New contributor



                  Kevin Dalmeijer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                  Check out our Code of Conduct.








                  answered 6 hours ago









                  Kevin DalmeijerKevin Dalmeijer

                  832 bronze badges




                  832 bronze badges




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                  New contributor




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                  Check out our Code of Conduct.























                      2












                      $begingroup$

                      As observed by Kevin Dalmeijer, you cannot expect an efficient method unless $P=NP$.



                      Since you're asking explicitly for dynamic programming: define $C(s,t,V)$ as the longest path from $s$ to $t$ without visiting the vertices in $V$. Values $C$ satisfy
                      beginalign*
                      C(s,t,V)=
                      begincases
                      max_uin N^-(t)setminus V C(s,u,Vcupt)+d_ut, & textif $sneq t$ and $N^-(t)setminus Vneq emptyset$,\
                      -infty, & textif $sneq t$ and $N^-(t)setminus V=emptyset$,\
                      0, & textif $s=t$,
                      endcases
                      endalign*

                      where $N^-(t)$ is the set of predecessors of vertex $t$, and $d_uv$ is the distance between $u$ and $v$. Computing $C$ for fixed $s$ takes time $O(n^22^n)$ and space $O(n2^n)$ and $C(s,t,emptyset)$ gives the longest path between $s$ and $t$.






                      share|improve this answer









                      $endgroup$

















                        2












                        $begingroup$

                        As observed by Kevin Dalmeijer, you cannot expect an efficient method unless $P=NP$.



                        Since you're asking explicitly for dynamic programming: define $C(s,t,V)$ as the longest path from $s$ to $t$ without visiting the vertices in $V$. Values $C$ satisfy
                        beginalign*
                        C(s,t,V)=
                        begincases
                        max_uin N^-(t)setminus V C(s,u,Vcupt)+d_ut, & textif $sneq t$ and $N^-(t)setminus Vneq emptyset$,\
                        -infty, & textif $sneq t$ and $N^-(t)setminus V=emptyset$,\
                        0, & textif $s=t$,
                        endcases
                        endalign*

                        where $N^-(t)$ is the set of predecessors of vertex $t$, and $d_uv$ is the distance between $u$ and $v$. Computing $C$ for fixed $s$ takes time $O(n^22^n)$ and space $O(n2^n)$ and $C(s,t,emptyset)$ gives the longest path between $s$ and $t$.






                        share|improve this answer









                        $endgroup$















                          2












                          2








                          2





                          $begingroup$

                          As observed by Kevin Dalmeijer, you cannot expect an efficient method unless $P=NP$.



                          Since you're asking explicitly for dynamic programming: define $C(s,t,V)$ as the longest path from $s$ to $t$ without visiting the vertices in $V$. Values $C$ satisfy
                          beginalign*
                          C(s,t,V)=
                          begincases
                          max_uin N^-(t)setminus V C(s,u,Vcupt)+d_ut, & textif $sneq t$ and $N^-(t)setminus Vneq emptyset$,\
                          -infty, & textif $sneq t$ and $N^-(t)setminus V=emptyset$,\
                          0, & textif $s=t$,
                          endcases
                          endalign*

                          where $N^-(t)$ is the set of predecessors of vertex $t$, and $d_uv$ is the distance between $u$ and $v$. Computing $C$ for fixed $s$ takes time $O(n^22^n)$ and space $O(n2^n)$ and $C(s,t,emptyset)$ gives the longest path between $s$ and $t$.






                          share|improve this answer









                          $endgroup$



                          As observed by Kevin Dalmeijer, you cannot expect an efficient method unless $P=NP$.



                          Since you're asking explicitly for dynamic programming: define $C(s,t,V)$ as the longest path from $s$ to $t$ without visiting the vertices in $V$. Values $C$ satisfy
                          beginalign*
                          C(s,t,V)=
                          begincases
                          max_uin N^-(t)setminus V C(s,u,Vcupt)+d_ut, & textif $sneq t$ and $N^-(t)setminus Vneq emptyset$,\
                          -infty, & textif $sneq t$ and $N^-(t)setminus V=emptyset$,\
                          0, & textif $s=t$,
                          endcases
                          endalign*

                          where $N^-(t)$ is the set of predecessors of vertex $t$, and $d_uv$ is the distance between $u$ and $v$. Computing $C$ for fixed $s$ takes time $O(n^22^n)$ and space $O(n2^n)$ and $C(s,t,emptyset)$ gives the longest path between $s$ and $t$.







                          share|improve this answer












                          share|improve this answer



                          share|improve this answer










                          answered 4 hours ago









                          Marcus RittMarcus Ritt

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