General solution to sytem of ODE's

The name of the pictureThe name of the pictureThe name of the pictureClash Royale CLAN TAG#URR8PPP











up vote
1
down vote

favorite












I came across the following formula for the general solution of a system of first order ODE's of the form $x^'=Ax$



$sum_i^N sum_j^m b_i,je^lambda_it(sum_k^m-1 fract^kk!(A-lambda_i)^k)v_i,j $



Where $N$ is the number of distinct eigenvalues and $m$ each of their respective algebraic multiplicity.



It looks odd to me that any sum in $k$ is equally long, dosnt that imply that any chain and thus any block is of same size ?







share|cite|improve this question


























    up vote
    1
    down vote

    favorite












    I came across the following formula for the general solution of a system of first order ODE's of the form $x^'=Ax$



    $sum_i^N sum_j^m b_i,je^lambda_it(sum_k^m-1 fract^kk!(A-lambda_i)^k)v_i,j $



    Where $N$ is the number of distinct eigenvalues and $m$ each of their respective algebraic multiplicity.



    It looks odd to me that any sum in $k$ is equally long, dosnt that imply that any chain and thus any block is of same size ?







    share|cite|improve this question
























      up vote
      1
      down vote

      favorite









      up vote
      1
      down vote

      favorite











      I came across the following formula for the general solution of a system of first order ODE's of the form $x^'=Ax$



      $sum_i^N sum_j^m b_i,je^lambda_it(sum_k^m-1 fract^kk!(A-lambda_i)^k)v_i,j $



      Where $N$ is the number of distinct eigenvalues and $m$ each of their respective algebraic multiplicity.



      It looks odd to me that any sum in $k$ is equally long, dosnt that imply that any chain and thus any block is of same size ?







      share|cite|improve this question














      I came across the following formula for the general solution of a system of first order ODE's of the form $x^'=Ax$



      $sum_i^N sum_j^m b_i,je^lambda_it(sum_k^m-1 fract^kk!(A-lambda_i)^k)v_i,j $



      Where $N$ is the number of distinct eigenvalues and $m$ each of their respective algebraic multiplicity.



      It looks odd to me that any sum in $k$ is equally long, dosnt that imply that any chain and thus any block is of same size ?









      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited Aug 15 at 16:53

























      asked Aug 12 at 7:42







      user561840



























          1 Answer
          1






          active

          oldest

          votes

















          up vote
          0
          down vote



          accepted
          +100










          They're not the same length, because $m$ is not truly a constant. You said it yourself: $m$ is the multiplicity of a given eigenvalue $lambda_i$, so its value is a function of $i$ (or of $lambda_i$ depending on your perspective). For this reason it should be written as $m_i$ or $m(lambda_i)$.



          Edit: As you note, it is possible to make the sums over $k$ shorter by choosing the $v_i,j$ carefully (i.e. choosing elements of $ker(A-lambda_i)$ whenever possible, then elements of $ker(A-lambda_i)^2$, and so forth). It's not strictly necessary, however, and this form for the solution avoids getting into that. It trades simplicity of solutions for simplicity of presentation.



          Basically you got confused here because you know too much linear algebra already :-)



          Here's a full description of the solution for posterity:



          • The first summation, $sum_i=1^N$, is over the distinct eigenvalues $lambda_1$, $ldots$, $lambda_n$. (This may include complex eigenvalues.) Each $lambda_i$ has a generalized eigenspace $V_i$, consisting of those vectors $vinmathbb R^n$ (or $mathbbC^n$ if complex eigenvalues are necessary) where $(A-lambda_i)^k v=0$ for some $k$.

          • The second summation, $sum_j=1^m(i)$, is over any basis $v_i,1, ldots, v_i,m(i)$ for $V_i$ (so $m(i) = dim V_i$).
            Together the set of vectors $v_i,j : 1le ile ntextrm and 1le jle m(i)$ is a basis for all of $mathbbR^n$ [$mathbbC^n$].

          • The constants $b_i,jinmathbbR$ [$mathbbC$] are chosen so that $x(0) = sum_i=1^N sum_j=1^m(i) b_i,jv_i,j$ (which can always be done uniquely; that's what it means for the $v_i,j$ to form a basis).

          • The third sum, $sum_k=0^m(i)-1 fract^kk! (A-lambda_i)^k v_i,j$, is simply equal to $exp((A-lambda_i)t)v_i,j$. This works because $exp((A - lambda_i)t)v_i,j$ can be written as the infinite series $sum_k=0^infty fract^kk! (A-lambda_i)^k v_i,j$, but all the terms where $kge m(i)$ vanish. (Side note: We know from the definition of generalized eigenspace that the terms of the series must vanish eventually, and in fact they must vanish by term $m(i)$. This can be seen by considering the sequence kernels of $(A-lambda_i)^k$ as $k$ ranges over the positive integers; each is a subspace of the next, and once two adjacent terms are equal, all future terms must be as well. You might want to think through why those statements are true and why they imply the statement.)





          share|cite|improve this answer






















          • So is $m$ also a function of $j$? Or is every summand in $j$ really $m-1$ long? Each $j$ corresponds to a repetition of the eignvalue right?
            – user561840
            Aug 16 at 6:26











          • Now that you mention it, the first and second $m$ should be represented by different symbols. I'll edit the answer.
            – Chad Groft
            Aug 16 at 15:37










          • Never mind, I see what the solution is doing now.
            – Chad Groft
            Aug 16 at 15:38










          • Just to clarify, each "third sum" corresponds to a Jordan block right?
            – user561840
            Aug 22 at 6:08






          • 1




            No. Jordan blocks aren't used at all here, just generalized eigenspaces.
            – Chad Groft
            2 days ago










          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: "69"
          ;
          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',
          convertImagesToLinks: true,
          noModals: false,
          showLowRepImageUploadWarning: true,
          reputationToPostImages: 10,
          bindNavPrevention: true,
          postfix: "",
          noCode: true, onDemand: true,
          discardSelector: ".discard-answer"
          ,immediatelyShowMarkdownHelp:true
          );



          );








           

          draft saved


          draft discarded


















          StackExchange.ready(
          function ()
          StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f2880072%2fgeneral-solution-to-sytem-of-odes%23new-answer', 'question_page');

          );

          Post as a guest





























          1 Answer
          1






          active

          oldest

          votes








          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes








          up vote
          0
          down vote



          accepted
          +100










          They're not the same length, because $m$ is not truly a constant. You said it yourself: $m$ is the multiplicity of a given eigenvalue $lambda_i$, so its value is a function of $i$ (or of $lambda_i$ depending on your perspective). For this reason it should be written as $m_i$ or $m(lambda_i)$.



          Edit: As you note, it is possible to make the sums over $k$ shorter by choosing the $v_i,j$ carefully (i.e. choosing elements of $ker(A-lambda_i)$ whenever possible, then elements of $ker(A-lambda_i)^2$, and so forth). It's not strictly necessary, however, and this form for the solution avoids getting into that. It trades simplicity of solutions for simplicity of presentation.



          Basically you got confused here because you know too much linear algebra already :-)



          Here's a full description of the solution for posterity:



          • The first summation, $sum_i=1^N$, is over the distinct eigenvalues $lambda_1$, $ldots$, $lambda_n$. (This may include complex eigenvalues.) Each $lambda_i$ has a generalized eigenspace $V_i$, consisting of those vectors $vinmathbb R^n$ (or $mathbbC^n$ if complex eigenvalues are necessary) where $(A-lambda_i)^k v=0$ for some $k$.

          • The second summation, $sum_j=1^m(i)$, is over any basis $v_i,1, ldots, v_i,m(i)$ for $V_i$ (so $m(i) = dim V_i$).
            Together the set of vectors $v_i,j : 1le ile ntextrm and 1le jle m(i)$ is a basis for all of $mathbbR^n$ [$mathbbC^n$].

          • The constants $b_i,jinmathbbR$ [$mathbbC$] are chosen so that $x(0) = sum_i=1^N sum_j=1^m(i) b_i,jv_i,j$ (which can always be done uniquely; that's what it means for the $v_i,j$ to form a basis).

          • The third sum, $sum_k=0^m(i)-1 fract^kk! (A-lambda_i)^k v_i,j$, is simply equal to $exp((A-lambda_i)t)v_i,j$. This works because $exp((A - lambda_i)t)v_i,j$ can be written as the infinite series $sum_k=0^infty fract^kk! (A-lambda_i)^k v_i,j$, but all the terms where $kge m(i)$ vanish. (Side note: We know from the definition of generalized eigenspace that the terms of the series must vanish eventually, and in fact they must vanish by term $m(i)$. This can be seen by considering the sequence kernels of $(A-lambda_i)^k$ as $k$ ranges over the positive integers; each is a subspace of the next, and once two adjacent terms are equal, all future terms must be as well. You might want to think through why those statements are true and why they imply the statement.)





          share|cite|improve this answer






















          • So is $m$ also a function of $j$? Or is every summand in $j$ really $m-1$ long? Each $j$ corresponds to a repetition of the eignvalue right?
            – user561840
            Aug 16 at 6:26











          • Now that you mention it, the first and second $m$ should be represented by different symbols. I'll edit the answer.
            – Chad Groft
            Aug 16 at 15:37










          • Never mind, I see what the solution is doing now.
            – Chad Groft
            Aug 16 at 15:38










          • Just to clarify, each "third sum" corresponds to a Jordan block right?
            – user561840
            Aug 22 at 6:08






          • 1




            No. Jordan blocks aren't used at all here, just generalized eigenspaces.
            – Chad Groft
            2 days ago














          up vote
          0
          down vote



          accepted
          +100










          They're not the same length, because $m$ is not truly a constant. You said it yourself: $m$ is the multiplicity of a given eigenvalue $lambda_i$, so its value is a function of $i$ (or of $lambda_i$ depending on your perspective). For this reason it should be written as $m_i$ or $m(lambda_i)$.



          Edit: As you note, it is possible to make the sums over $k$ shorter by choosing the $v_i,j$ carefully (i.e. choosing elements of $ker(A-lambda_i)$ whenever possible, then elements of $ker(A-lambda_i)^2$, and so forth). It's not strictly necessary, however, and this form for the solution avoids getting into that. It trades simplicity of solutions for simplicity of presentation.



          Basically you got confused here because you know too much linear algebra already :-)



          Here's a full description of the solution for posterity:



          • The first summation, $sum_i=1^N$, is over the distinct eigenvalues $lambda_1$, $ldots$, $lambda_n$. (This may include complex eigenvalues.) Each $lambda_i$ has a generalized eigenspace $V_i$, consisting of those vectors $vinmathbb R^n$ (or $mathbbC^n$ if complex eigenvalues are necessary) where $(A-lambda_i)^k v=0$ for some $k$.

          • The second summation, $sum_j=1^m(i)$, is over any basis $v_i,1, ldots, v_i,m(i)$ for $V_i$ (so $m(i) = dim V_i$).
            Together the set of vectors $v_i,j : 1le ile ntextrm and 1le jle m(i)$ is a basis for all of $mathbbR^n$ [$mathbbC^n$].

          • The constants $b_i,jinmathbbR$ [$mathbbC$] are chosen so that $x(0) = sum_i=1^N sum_j=1^m(i) b_i,jv_i,j$ (which can always be done uniquely; that's what it means for the $v_i,j$ to form a basis).

          • The third sum, $sum_k=0^m(i)-1 fract^kk! (A-lambda_i)^k v_i,j$, is simply equal to $exp((A-lambda_i)t)v_i,j$. This works because $exp((A - lambda_i)t)v_i,j$ can be written as the infinite series $sum_k=0^infty fract^kk! (A-lambda_i)^k v_i,j$, but all the terms where $kge m(i)$ vanish. (Side note: We know from the definition of generalized eigenspace that the terms of the series must vanish eventually, and in fact they must vanish by term $m(i)$. This can be seen by considering the sequence kernels of $(A-lambda_i)^k$ as $k$ ranges over the positive integers; each is a subspace of the next, and once two adjacent terms are equal, all future terms must be as well. You might want to think through why those statements are true and why they imply the statement.)





          share|cite|improve this answer






















          • So is $m$ also a function of $j$? Or is every summand in $j$ really $m-1$ long? Each $j$ corresponds to a repetition of the eignvalue right?
            – user561840
            Aug 16 at 6:26











          • Now that you mention it, the first and second $m$ should be represented by different symbols. I'll edit the answer.
            – Chad Groft
            Aug 16 at 15:37










          • Never mind, I see what the solution is doing now.
            – Chad Groft
            Aug 16 at 15:38










          • Just to clarify, each "third sum" corresponds to a Jordan block right?
            – user561840
            Aug 22 at 6:08






          • 1




            No. Jordan blocks aren't used at all here, just generalized eigenspaces.
            – Chad Groft
            2 days ago












          up vote
          0
          down vote



          accepted
          +100







          up vote
          0
          down vote



          accepted
          +100




          +100




          They're not the same length, because $m$ is not truly a constant. You said it yourself: $m$ is the multiplicity of a given eigenvalue $lambda_i$, so its value is a function of $i$ (or of $lambda_i$ depending on your perspective). For this reason it should be written as $m_i$ or $m(lambda_i)$.



          Edit: As you note, it is possible to make the sums over $k$ shorter by choosing the $v_i,j$ carefully (i.e. choosing elements of $ker(A-lambda_i)$ whenever possible, then elements of $ker(A-lambda_i)^2$, and so forth). It's not strictly necessary, however, and this form for the solution avoids getting into that. It trades simplicity of solutions for simplicity of presentation.



          Basically you got confused here because you know too much linear algebra already :-)



          Here's a full description of the solution for posterity:



          • The first summation, $sum_i=1^N$, is over the distinct eigenvalues $lambda_1$, $ldots$, $lambda_n$. (This may include complex eigenvalues.) Each $lambda_i$ has a generalized eigenspace $V_i$, consisting of those vectors $vinmathbb R^n$ (or $mathbbC^n$ if complex eigenvalues are necessary) where $(A-lambda_i)^k v=0$ for some $k$.

          • The second summation, $sum_j=1^m(i)$, is over any basis $v_i,1, ldots, v_i,m(i)$ for $V_i$ (so $m(i) = dim V_i$).
            Together the set of vectors $v_i,j : 1le ile ntextrm and 1le jle m(i)$ is a basis for all of $mathbbR^n$ [$mathbbC^n$].

          • The constants $b_i,jinmathbbR$ [$mathbbC$] are chosen so that $x(0) = sum_i=1^N sum_j=1^m(i) b_i,jv_i,j$ (which can always be done uniquely; that's what it means for the $v_i,j$ to form a basis).

          • The third sum, $sum_k=0^m(i)-1 fract^kk! (A-lambda_i)^k v_i,j$, is simply equal to $exp((A-lambda_i)t)v_i,j$. This works because $exp((A - lambda_i)t)v_i,j$ can be written as the infinite series $sum_k=0^infty fract^kk! (A-lambda_i)^k v_i,j$, but all the terms where $kge m(i)$ vanish. (Side note: We know from the definition of generalized eigenspace that the terms of the series must vanish eventually, and in fact they must vanish by term $m(i)$. This can be seen by considering the sequence kernels of $(A-lambda_i)^k$ as $k$ ranges over the positive integers; each is a subspace of the next, and once two adjacent terms are equal, all future terms must be as well. You might want to think through why those statements are true and why they imply the statement.)





          share|cite|improve this answer














          They're not the same length, because $m$ is not truly a constant. You said it yourself: $m$ is the multiplicity of a given eigenvalue $lambda_i$, so its value is a function of $i$ (or of $lambda_i$ depending on your perspective). For this reason it should be written as $m_i$ or $m(lambda_i)$.



          Edit: As you note, it is possible to make the sums over $k$ shorter by choosing the $v_i,j$ carefully (i.e. choosing elements of $ker(A-lambda_i)$ whenever possible, then elements of $ker(A-lambda_i)^2$, and so forth). It's not strictly necessary, however, and this form for the solution avoids getting into that. It trades simplicity of solutions for simplicity of presentation.



          Basically you got confused here because you know too much linear algebra already :-)



          Here's a full description of the solution for posterity:



          • The first summation, $sum_i=1^N$, is over the distinct eigenvalues $lambda_1$, $ldots$, $lambda_n$. (This may include complex eigenvalues.) Each $lambda_i$ has a generalized eigenspace $V_i$, consisting of those vectors $vinmathbb R^n$ (or $mathbbC^n$ if complex eigenvalues are necessary) where $(A-lambda_i)^k v=0$ for some $k$.

          • The second summation, $sum_j=1^m(i)$, is over any basis $v_i,1, ldots, v_i,m(i)$ for $V_i$ (so $m(i) = dim V_i$).
            Together the set of vectors $v_i,j : 1le ile ntextrm and 1le jle m(i)$ is a basis for all of $mathbbR^n$ [$mathbbC^n$].

          • The constants $b_i,jinmathbbR$ [$mathbbC$] are chosen so that $x(0) = sum_i=1^N sum_j=1^m(i) b_i,jv_i,j$ (which can always be done uniquely; that's what it means for the $v_i,j$ to form a basis).

          • The third sum, $sum_k=0^m(i)-1 fract^kk! (A-lambda_i)^k v_i,j$, is simply equal to $exp((A-lambda_i)t)v_i,j$. This works because $exp((A - lambda_i)t)v_i,j$ can be written as the infinite series $sum_k=0^infty fract^kk! (A-lambda_i)^k v_i,j$, but all the terms where $kge m(i)$ vanish. (Side note: We know from the definition of generalized eigenspace that the terms of the series must vanish eventually, and in fact they must vanish by term $m(i)$. This can be seen by considering the sequence kernels of $(A-lambda_i)^k$ as $k$ ranges over the positive integers; each is a subspace of the next, and once two adjacent terms are equal, all future terms must be as well. You might want to think through why those statements are true and why they imply the statement.)






          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited Aug 16 at 16:18

























          answered Aug 15 at 19:18









          Chad Groft

          41327




          41327











          • So is $m$ also a function of $j$? Or is every summand in $j$ really $m-1$ long? Each $j$ corresponds to a repetition of the eignvalue right?
            – user561840
            Aug 16 at 6:26











          • Now that you mention it, the first and second $m$ should be represented by different symbols. I'll edit the answer.
            – Chad Groft
            Aug 16 at 15:37










          • Never mind, I see what the solution is doing now.
            – Chad Groft
            Aug 16 at 15:38










          • Just to clarify, each "third sum" corresponds to a Jordan block right?
            – user561840
            Aug 22 at 6:08






          • 1




            No. Jordan blocks aren't used at all here, just generalized eigenspaces.
            – Chad Groft
            2 days ago
















          • So is $m$ also a function of $j$? Or is every summand in $j$ really $m-1$ long? Each $j$ corresponds to a repetition of the eignvalue right?
            – user561840
            Aug 16 at 6:26











          • Now that you mention it, the first and second $m$ should be represented by different symbols. I'll edit the answer.
            – Chad Groft
            Aug 16 at 15:37










          • Never mind, I see what the solution is doing now.
            – Chad Groft
            Aug 16 at 15:38










          • Just to clarify, each "third sum" corresponds to a Jordan block right?
            – user561840
            Aug 22 at 6:08






          • 1




            No. Jordan blocks aren't used at all here, just generalized eigenspaces.
            – Chad Groft
            2 days ago















          So is $m$ also a function of $j$? Or is every summand in $j$ really $m-1$ long? Each $j$ corresponds to a repetition of the eignvalue right?
          – user561840
          Aug 16 at 6:26





          So is $m$ also a function of $j$? Or is every summand in $j$ really $m-1$ long? Each $j$ corresponds to a repetition of the eignvalue right?
          – user561840
          Aug 16 at 6:26













          Now that you mention it, the first and second $m$ should be represented by different symbols. I'll edit the answer.
          – Chad Groft
          Aug 16 at 15:37




          Now that you mention it, the first and second $m$ should be represented by different symbols. I'll edit the answer.
          – Chad Groft
          Aug 16 at 15:37












          Never mind, I see what the solution is doing now.
          – Chad Groft
          Aug 16 at 15:38




          Never mind, I see what the solution is doing now.
          – Chad Groft
          Aug 16 at 15:38












          Just to clarify, each "third sum" corresponds to a Jordan block right?
          – user561840
          Aug 22 at 6:08




          Just to clarify, each "third sum" corresponds to a Jordan block right?
          – user561840
          Aug 22 at 6:08




          1




          1




          No. Jordan blocks aren't used at all here, just generalized eigenspaces.
          – Chad Groft
          2 days ago




          No. Jordan blocks aren't used at all here, just generalized eigenspaces.
          – Chad Groft
          2 days ago












           

          draft saved


          draft discarded


























           


          draft saved


          draft discarded














          StackExchange.ready(
          function ()
          StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f2880072%2fgeneral-solution-to-sytem-of-odes%23new-answer', 'question_page');

          );

          Post as a guest













































































          這個網誌中的熱門文章

          How to combine Bézier curves to a surface?

          Carbon dioxide

          Why am i infinitely getting the same tweet with the Twitter Search API?