Computing Cardinality of Sumsets using Convolutions and FFT












1












$begingroup$


I read somewhere that you can compute the cardinality of sumsets by computing the convolutions of the characteristic vectors of the given sets, and that this can be done efficiently using Fast Fourier Transform. How would this work?










share|cite|improve this question









$endgroup$

















    1












    $begingroup$


    I read somewhere that you can compute the cardinality of sumsets by computing the convolutions of the characteristic vectors of the given sets, and that this can be done efficiently using Fast Fourier Transform. How would this work?










    share|cite|improve this question









    $endgroup$















      1












      1








      1





      $begingroup$


      I read somewhere that you can compute the cardinality of sumsets by computing the convolutions of the characteristic vectors of the given sets, and that this can be done efficiently using Fast Fourier Transform. How would this work?










      share|cite|improve this question









      $endgroup$




      I read somewhere that you can compute the cardinality of sumsets by computing the convolutions of the characteristic vectors of the given sets, and that this can be done efficiently using Fast Fourier Transform. How would this work?







      algorithms






      share|cite|improve this question













      share|cite|improve this question











      share|cite|improve this question




      share|cite|improve this question










      asked Feb 25 at 4:28









      Arnaud AvondetArnaud Avondet

      83




      83






















          1 Answer
          1






          active

          oldest

          votes


















          2












          $begingroup$

          Here is a sketch of the main ideas. Let $S,T subseteq mathbb{N}$ be two multisets of non-negative integers, and define $S+T$ to be the multiset



          $$S+T = {s+t mid s in S, t in T}.$$



          Let $chi_S$ represent the characteristic vector of a set $S$. Then



          $$chi_{S+T} = chi_S * chi_T,$$



          where $*$ is a convolution operator.



          The Fourier transform $mathcal{F}$ has the property that



          $$mathcal{F}(f*g) = mathcal{F}(f) times mathcal{F}(g),$$



          where $times$ represents pointwise multiplication of functions. Therefore,



          $$mathcal{F}(chi_{S+T}) = mathcal{F}(chi_S) times mathcal{F}(chi_T),$$



          and in particular,



          $$chi_{S+T} = mathcal{F}^{-1}(mathcal{F}(chi_S) times mathcal{F}(chi_T)).$$



          This gives us a method to compute $S+T$. We can compute the characteristic vector $chi_{S+T}$ for the multiset $S+T$ by computing $mathcal{F}(chi_S)$, the Fourier transform of the characteristic vector for $S$, and $mathcal{F}(g)$; multiplying them; and then applying the inverse Fourier transform to the result. Each of these steps can be implemented efficiently using the Fast Fourier transform. The end result will be $chi_{S+T}$, from which we can reconstruct $S+T$ or its cardinality.



          The overall running time will be $O(n log n)$, where $n$ is an upper bound on the largest element of $S,T$.






          share|cite|improve this answer









          $endgroup$














            Your Answer





            StackExchange.ifUsing("editor", function () {
            return StackExchange.using("mathjaxEditing", function () {
            StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
            StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
            });
            });
            }, "mathjax-editing");

            StackExchange.ready(function() {
            var channelOptions = {
            tags: "".split(" "),
            id: "419"
            };
            initTagRenderer("".split(" "), "".split(" "), channelOptions);

            StackExchange.using("externalEditor", function() {
            // Have to fire editor after snippets, if snippets enabled
            if (StackExchange.settings.snippets.snippetsEnabled) {
            StackExchange.using("snippets", function() {
            createEditor();
            });
            }
            else {
            createEditor();
            }
            });

            function createEditor() {
            StackExchange.prepareEditor({
            heartbeatType: 'answer',
            autoActivateHeartbeat: false,
            convertImagesToLinks: false,
            noModals: true,
            showLowRepImageUploadWarning: true,
            reputationToPostImages: null,
            bindNavPrevention: true,
            postfix: "",
            imageUploader: {
            brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
            contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
            allowUrls: true
            },
            onDemand: true,
            discardSelector: ".discard-answer"
            ,immediatelyShowMarkdownHelp:true
            });


            }
            });














            draft saved

            draft discarded


















            StackExchange.ready(
            function () {
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fcs.stackexchange.com%2fquestions%2f104817%2fcomputing-cardinality-of-sumsets-using-convolutions-and-fft%23new-answer', 'question_page');
            }
            );

            Post as a guest















            Required, but never shown

























            1 Answer
            1






            active

            oldest

            votes








            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            2












            $begingroup$

            Here is a sketch of the main ideas. Let $S,T subseteq mathbb{N}$ be two multisets of non-negative integers, and define $S+T$ to be the multiset



            $$S+T = {s+t mid s in S, t in T}.$$



            Let $chi_S$ represent the characteristic vector of a set $S$. Then



            $$chi_{S+T} = chi_S * chi_T,$$



            where $*$ is a convolution operator.



            The Fourier transform $mathcal{F}$ has the property that



            $$mathcal{F}(f*g) = mathcal{F}(f) times mathcal{F}(g),$$



            where $times$ represents pointwise multiplication of functions. Therefore,



            $$mathcal{F}(chi_{S+T}) = mathcal{F}(chi_S) times mathcal{F}(chi_T),$$



            and in particular,



            $$chi_{S+T} = mathcal{F}^{-1}(mathcal{F}(chi_S) times mathcal{F}(chi_T)).$$



            This gives us a method to compute $S+T$. We can compute the characteristic vector $chi_{S+T}$ for the multiset $S+T$ by computing $mathcal{F}(chi_S)$, the Fourier transform of the characteristic vector for $S$, and $mathcal{F}(g)$; multiplying them; and then applying the inverse Fourier transform to the result. Each of these steps can be implemented efficiently using the Fast Fourier transform. The end result will be $chi_{S+T}$, from which we can reconstruct $S+T$ or its cardinality.



            The overall running time will be $O(n log n)$, where $n$ is an upper bound on the largest element of $S,T$.






            share|cite|improve this answer









            $endgroup$


















              2












              $begingroup$

              Here is a sketch of the main ideas. Let $S,T subseteq mathbb{N}$ be two multisets of non-negative integers, and define $S+T$ to be the multiset



              $$S+T = {s+t mid s in S, t in T}.$$



              Let $chi_S$ represent the characteristic vector of a set $S$. Then



              $$chi_{S+T} = chi_S * chi_T,$$



              where $*$ is a convolution operator.



              The Fourier transform $mathcal{F}$ has the property that



              $$mathcal{F}(f*g) = mathcal{F}(f) times mathcal{F}(g),$$



              where $times$ represents pointwise multiplication of functions. Therefore,



              $$mathcal{F}(chi_{S+T}) = mathcal{F}(chi_S) times mathcal{F}(chi_T),$$



              and in particular,



              $$chi_{S+T} = mathcal{F}^{-1}(mathcal{F}(chi_S) times mathcal{F}(chi_T)).$$



              This gives us a method to compute $S+T$. We can compute the characteristic vector $chi_{S+T}$ for the multiset $S+T$ by computing $mathcal{F}(chi_S)$, the Fourier transform of the characteristic vector for $S$, and $mathcal{F}(g)$; multiplying them; and then applying the inverse Fourier transform to the result. Each of these steps can be implemented efficiently using the Fast Fourier transform. The end result will be $chi_{S+T}$, from which we can reconstruct $S+T$ or its cardinality.



              The overall running time will be $O(n log n)$, where $n$ is an upper bound on the largest element of $S,T$.






              share|cite|improve this answer









              $endgroup$
















                2












                2








                2





                $begingroup$

                Here is a sketch of the main ideas. Let $S,T subseteq mathbb{N}$ be two multisets of non-negative integers, and define $S+T$ to be the multiset



                $$S+T = {s+t mid s in S, t in T}.$$



                Let $chi_S$ represent the characteristic vector of a set $S$. Then



                $$chi_{S+T} = chi_S * chi_T,$$



                where $*$ is a convolution operator.



                The Fourier transform $mathcal{F}$ has the property that



                $$mathcal{F}(f*g) = mathcal{F}(f) times mathcal{F}(g),$$



                where $times$ represents pointwise multiplication of functions. Therefore,



                $$mathcal{F}(chi_{S+T}) = mathcal{F}(chi_S) times mathcal{F}(chi_T),$$



                and in particular,



                $$chi_{S+T} = mathcal{F}^{-1}(mathcal{F}(chi_S) times mathcal{F}(chi_T)).$$



                This gives us a method to compute $S+T$. We can compute the characteristic vector $chi_{S+T}$ for the multiset $S+T$ by computing $mathcal{F}(chi_S)$, the Fourier transform of the characteristic vector for $S$, and $mathcal{F}(g)$; multiplying them; and then applying the inverse Fourier transform to the result. Each of these steps can be implemented efficiently using the Fast Fourier transform. The end result will be $chi_{S+T}$, from which we can reconstruct $S+T$ or its cardinality.



                The overall running time will be $O(n log n)$, where $n$ is an upper bound on the largest element of $S,T$.






                share|cite|improve this answer









                $endgroup$



                Here is a sketch of the main ideas. Let $S,T subseteq mathbb{N}$ be two multisets of non-negative integers, and define $S+T$ to be the multiset



                $$S+T = {s+t mid s in S, t in T}.$$



                Let $chi_S$ represent the characteristic vector of a set $S$. Then



                $$chi_{S+T} = chi_S * chi_T,$$



                where $*$ is a convolution operator.



                The Fourier transform $mathcal{F}$ has the property that



                $$mathcal{F}(f*g) = mathcal{F}(f) times mathcal{F}(g),$$



                where $times$ represents pointwise multiplication of functions. Therefore,



                $$mathcal{F}(chi_{S+T}) = mathcal{F}(chi_S) times mathcal{F}(chi_T),$$



                and in particular,



                $$chi_{S+T} = mathcal{F}^{-1}(mathcal{F}(chi_S) times mathcal{F}(chi_T)).$$



                This gives us a method to compute $S+T$. We can compute the characteristic vector $chi_{S+T}$ for the multiset $S+T$ by computing $mathcal{F}(chi_S)$, the Fourier transform of the characteristic vector for $S$, and $mathcal{F}(g)$; multiplying them; and then applying the inverse Fourier transform to the result. Each of these steps can be implemented efficiently using the Fast Fourier transform. The end result will be $chi_{S+T}$, from which we can reconstruct $S+T$ or its cardinality.



                The overall running time will be $O(n log n)$, where $n$ is an upper bound on the largest element of $S,T$.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Feb 25 at 5:52









                D.W.D.W.

                102k12129293




                102k12129293






























                    draft saved

                    draft discarded




















































                    Thanks for contributing an answer to Computer Science Stack Exchange!


                    • Please be sure to answer the question. Provide details and share your research!

                    But avoid



                    • Asking for help, clarification, or responding to other answers.

                    • Making statements based on opinion; back them up with references or personal experience.


                    Use MathJax to format equations. MathJax reference.


                    To learn more, see our tips on writing great answers.




                    draft saved


                    draft discarded














                    StackExchange.ready(
                    function () {
                    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fcs.stackexchange.com%2fquestions%2f104817%2fcomputing-cardinality-of-sumsets-using-convolutions-and-fft%23new-answer', 'question_page');
                    }
                    );

                    Post as a guest















                    Required, but never shown





















































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown

































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown







                    Popular posts from this blog

                    Probability when a professor distributes a quiz and homework assignment to a class of n students.

                    Aardman Animations

                    Are they similar matrix