linear isometric embedding from $(mathbb{R}^2, | |_2)$ to $(l^1, | |_1)$












7














I would like to prove the following :




There isn't a linear isometric embedding from $(mathbb{R}^2, | cdot |_2)$ to $(l^1, | cdot |_1)$




I don't know how to prove this. So far I am able to prove this result only in the case where the vector $(1,0)$ and $(0,1)$ are sent to sequences that have all positive, or all negative value.



In this case I use the fact that the $2$ norm is not linear whereas the $1$ norm is (ie $| xa + yb | = xa + yb$, $x, y, a, b > 0$).



The problem is that when the sequences have different signs i's hard for me to conclude.



Thank you.










share|cite|improve this question



























    7














    I would like to prove the following :




    There isn't a linear isometric embedding from $(mathbb{R}^2, | cdot |_2)$ to $(l^1, | cdot |_1)$




    I don't know how to prove this. So far I am able to prove this result only in the case where the vector $(1,0)$ and $(0,1)$ are sent to sequences that have all positive, or all negative value.



    In this case I use the fact that the $2$ norm is not linear whereas the $1$ norm is (ie $| xa + yb | = xa + yb$, $x, y, a, b > 0$).



    The problem is that when the sequences have different signs i's hard for me to conclude.



    Thank you.










    share|cite|improve this question

























      7












      7








      7


      2





      I would like to prove the following :




      There isn't a linear isometric embedding from $(mathbb{R}^2, | cdot |_2)$ to $(l^1, | cdot |_1)$




      I don't know how to prove this. So far I am able to prove this result only in the case where the vector $(1,0)$ and $(0,1)$ are sent to sequences that have all positive, or all negative value.



      In this case I use the fact that the $2$ norm is not linear whereas the $1$ norm is (ie $| xa + yb | = xa + yb$, $x, y, a, b > 0$).



      The problem is that when the sequences have different signs i's hard for me to conclude.



      Thank you.










      share|cite|improve this question













      I would like to prove the following :




      There isn't a linear isometric embedding from $(mathbb{R}^2, | cdot |_2)$ to $(l^1, | cdot |_1)$




      I don't know how to prove this. So far I am able to prove this result only in the case where the vector $(1,0)$ and $(0,1)$ are sent to sequences that have all positive, or all negative value.



      In this case I use the fact that the $2$ norm is not linear whereas the $1$ norm is (ie $| xa + yb | = xa + yb$, $x, y, a, b > 0$).



      The problem is that when the sequences have different signs i's hard for me to conclude.



      Thank you.







      real-analysis linear-algebra general-topology functional-analysis normed-spaces






      share|cite|improve this question













      share|cite|improve this question











      share|cite|improve this question




      share|cite|improve this question










      asked Nov 29 at 9:18









      DP_q

      856




      856






















          2 Answers
          2






          active

          oldest

          votes


















          2














          Let the image of $(1,0)$ be the sequence $x=(x_i)_i$ and
          let the image of $(0,1)$ be the sequence $y=(y_i)_i$.
          Now we define the function
          For $alphainmathbb R$ we define
          $$
          f(alpha) = | x + alpha y| = sum_{i=1}^infty | x_i+alpha y_i |.
          $$

          Note that because of the isometry we should have
          $$
          f(alpha) = sqrt{1+alpha^2}.
          $$

          We cannot say that $f$ is linear, because as $alpha$ changes, there might be sign changes in some of the terms $|x_i+alpha y_i|$.



          We pick a fixed $j$ such that $y_jneq0$.
          Then we choose $Ninmathbb N$ such that
          $sum_{i=N+1}^infty |y_i| < |y_j|/2$.
          Clearly, $Ngeq j$ has to be true.
          We define
          $$
          g(alpha):=sum_{i=1}^N | x_i+alpha y_i |
          qquad
          h(alpha):=sum_{i=N+1}^infty | x_i+alpha y_i |.
          $$

          Clearly, $f=g+h$.
          It can be shown that $h(alpha)$ is Lipschitz continuous with the global Lipschitz constant $|y_j|/2$.



          Now we will analyse $g$.
          Since there are only finitely many terms, there are only finitely many points where $g$ is not smooth.
          In between those points $g$ is affine linear.
          If we consider the point $alpha_j:=-x_j/y_j$, then we can find $varepsilon_0>0$ such that
          $g$ is affine linear on the intervals
          $[alpha_j-varepsilon_0,alpha_j]$ and $[alpha_j,alpha_j+varepsilon_0]$.
          Then we have
          $$
          g(alpha_j+varepsilon)-g(alpha_j) geq varepsilon |y_j|
          quadtext{and}quad
          g(alpha_j-varepsilon)-g(alpha_j) geq varepsilon |y_j|
          $$

          for all $varepsilonin (0,varepsilon_0)$.



          Combining this with the Lipschitz constant for $h$, we can conclude
          $$
          f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j) geq varepsilon | y_j|.
          $$

          for all $varepsilonin (0,varepsilon_0)$.
          This means that
          $$
          liminf_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          geq |y_j| > 0.
          $$

          holds.
          However, this is a contradiction to $ f(alpha) = sqrt{1+alpha^2}$, because here the right-hand term is twice continuously differentiable, which (by using Taylor expansion) implies
          $$
          lim_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          =0
          $$






          share|cite|improve this answer























          • That is a good point. I think i found a fix and will update soon
            – supinf
            Nov 29 at 11:15










          • @supinf To get a contradiction you can simply use the fact that : $sum_{i = 1}^{infty} mid x_i + x cdot y_i mid$ is convex whereas $sqrt{x^2+1}-mid x_0 + x cdot y_0 mid$ is not convex ?
            – Thinking
            Nov 29 at 14:05



















          0














          The following construction gives an approximate such imbedding. Choose an $Ngg1$, and put
          $$f(1,0)=(x_k)_{0leq kleq N-1},qquad f(0,1)=(y_k)_{0leq kleq N-1}$$
          with
          $$x_k:={piover 2N}cos{2pi kover N},quad y_k:={piover 2N}sin{2pi kover N}qquad(0leq kleq N-1) .$$
          Then, by linearity,
          $$eqalign{|f(cosphi,sinphi)|&={piover 2N}sum_{k=0}^{N-1}left|cosphicos{2kpiover N}+sinphisin{2kpiover N}right| cr &=
          {1over 4}sum_{k=0}^{N-1}{2piover N}left|cosleft(phi-{2pi kover N}right)right|cr &approx{1over4}int_{-pi}^pi|cos t|>dt=1 ,cr}$$

          independently of $phi$.



          I'm not so sure that an exact isometric imbedding is impossible.






          share|cite|improve this answer





















          • It is impossible by a convexity argument (see my comment under @supinf answer). Nevertheless this a nice approximation how did you get that idea ?
            – Thinking
            Nov 29 at 15:06











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          2 Answers
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          active

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          2 Answers
          2






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          2














          Let the image of $(1,0)$ be the sequence $x=(x_i)_i$ and
          let the image of $(0,1)$ be the sequence $y=(y_i)_i$.
          Now we define the function
          For $alphainmathbb R$ we define
          $$
          f(alpha) = | x + alpha y| = sum_{i=1}^infty | x_i+alpha y_i |.
          $$

          Note that because of the isometry we should have
          $$
          f(alpha) = sqrt{1+alpha^2}.
          $$

          We cannot say that $f$ is linear, because as $alpha$ changes, there might be sign changes in some of the terms $|x_i+alpha y_i|$.



          We pick a fixed $j$ such that $y_jneq0$.
          Then we choose $Ninmathbb N$ such that
          $sum_{i=N+1}^infty |y_i| < |y_j|/2$.
          Clearly, $Ngeq j$ has to be true.
          We define
          $$
          g(alpha):=sum_{i=1}^N | x_i+alpha y_i |
          qquad
          h(alpha):=sum_{i=N+1}^infty | x_i+alpha y_i |.
          $$

          Clearly, $f=g+h$.
          It can be shown that $h(alpha)$ is Lipschitz continuous with the global Lipschitz constant $|y_j|/2$.



          Now we will analyse $g$.
          Since there are only finitely many terms, there are only finitely many points where $g$ is not smooth.
          In between those points $g$ is affine linear.
          If we consider the point $alpha_j:=-x_j/y_j$, then we can find $varepsilon_0>0$ such that
          $g$ is affine linear on the intervals
          $[alpha_j-varepsilon_0,alpha_j]$ and $[alpha_j,alpha_j+varepsilon_0]$.
          Then we have
          $$
          g(alpha_j+varepsilon)-g(alpha_j) geq varepsilon |y_j|
          quadtext{and}quad
          g(alpha_j-varepsilon)-g(alpha_j) geq varepsilon |y_j|
          $$

          for all $varepsilonin (0,varepsilon_0)$.



          Combining this with the Lipschitz constant for $h$, we can conclude
          $$
          f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j) geq varepsilon | y_j|.
          $$

          for all $varepsilonin (0,varepsilon_0)$.
          This means that
          $$
          liminf_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          geq |y_j| > 0.
          $$

          holds.
          However, this is a contradiction to $ f(alpha) = sqrt{1+alpha^2}$, because here the right-hand term is twice continuously differentiable, which (by using Taylor expansion) implies
          $$
          lim_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          =0
          $$






          share|cite|improve this answer























          • That is a good point. I think i found a fix and will update soon
            – supinf
            Nov 29 at 11:15










          • @supinf To get a contradiction you can simply use the fact that : $sum_{i = 1}^{infty} mid x_i + x cdot y_i mid$ is convex whereas $sqrt{x^2+1}-mid x_0 + x cdot y_0 mid$ is not convex ?
            – Thinking
            Nov 29 at 14:05
















          2














          Let the image of $(1,0)$ be the sequence $x=(x_i)_i$ and
          let the image of $(0,1)$ be the sequence $y=(y_i)_i$.
          Now we define the function
          For $alphainmathbb R$ we define
          $$
          f(alpha) = | x + alpha y| = sum_{i=1}^infty | x_i+alpha y_i |.
          $$

          Note that because of the isometry we should have
          $$
          f(alpha) = sqrt{1+alpha^2}.
          $$

          We cannot say that $f$ is linear, because as $alpha$ changes, there might be sign changes in some of the terms $|x_i+alpha y_i|$.



          We pick a fixed $j$ such that $y_jneq0$.
          Then we choose $Ninmathbb N$ such that
          $sum_{i=N+1}^infty |y_i| < |y_j|/2$.
          Clearly, $Ngeq j$ has to be true.
          We define
          $$
          g(alpha):=sum_{i=1}^N | x_i+alpha y_i |
          qquad
          h(alpha):=sum_{i=N+1}^infty | x_i+alpha y_i |.
          $$

          Clearly, $f=g+h$.
          It can be shown that $h(alpha)$ is Lipschitz continuous with the global Lipschitz constant $|y_j|/2$.



          Now we will analyse $g$.
          Since there are only finitely many terms, there are only finitely many points where $g$ is not smooth.
          In between those points $g$ is affine linear.
          If we consider the point $alpha_j:=-x_j/y_j$, then we can find $varepsilon_0>0$ such that
          $g$ is affine linear on the intervals
          $[alpha_j-varepsilon_0,alpha_j]$ and $[alpha_j,alpha_j+varepsilon_0]$.
          Then we have
          $$
          g(alpha_j+varepsilon)-g(alpha_j) geq varepsilon |y_j|
          quadtext{and}quad
          g(alpha_j-varepsilon)-g(alpha_j) geq varepsilon |y_j|
          $$

          for all $varepsilonin (0,varepsilon_0)$.



          Combining this with the Lipschitz constant for $h$, we can conclude
          $$
          f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j) geq varepsilon | y_j|.
          $$

          for all $varepsilonin (0,varepsilon_0)$.
          This means that
          $$
          liminf_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          geq |y_j| > 0.
          $$

          holds.
          However, this is a contradiction to $ f(alpha) = sqrt{1+alpha^2}$, because here the right-hand term is twice continuously differentiable, which (by using Taylor expansion) implies
          $$
          lim_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          =0
          $$






          share|cite|improve this answer























          • That is a good point. I think i found a fix and will update soon
            – supinf
            Nov 29 at 11:15










          • @supinf To get a contradiction you can simply use the fact that : $sum_{i = 1}^{infty} mid x_i + x cdot y_i mid$ is convex whereas $sqrt{x^2+1}-mid x_0 + x cdot y_0 mid$ is not convex ?
            – Thinking
            Nov 29 at 14:05














          2












          2








          2






          Let the image of $(1,0)$ be the sequence $x=(x_i)_i$ and
          let the image of $(0,1)$ be the sequence $y=(y_i)_i$.
          Now we define the function
          For $alphainmathbb R$ we define
          $$
          f(alpha) = | x + alpha y| = sum_{i=1}^infty | x_i+alpha y_i |.
          $$

          Note that because of the isometry we should have
          $$
          f(alpha) = sqrt{1+alpha^2}.
          $$

          We cannot say that $f$ is linear, because as $alpha$ changes, there might be sign changes in some of the terms $|x_i+alpha y_i|$.



          We pick a fixed $j$ such that $y_jneq0$.
          Then we choose $Ninmathbb N$ such that
          $sum_{i=N+1}^infty |y_i| < |y_j|/2$.
          Clearly, $Ngeq j$ has to be true.
          We define
          $$
          g(alpha):=sum_{i=1}^N | x_i+alpha y_i |
          qquad
          h(alpha):=sum_{i=N+1}^infty | x_i+alpha y_i |.
          $$

          Clearly, $f=g+h$.
          It can be shown that $h(alpha)$ is Lipschitz continuous with the global Lipschitz constant $|y_j|/2$.



          Now we will analyse $g$.
          Since there are only finitely many terms, there are only finitely many points where $g$ is not smooth.
          In between those points $g$ is affine linear.
          If we consider the point $alpha_j:=-x_j/y_j$, then we can find $varepsilon_0>0$ such that
          $g$ is affine linear on the intervals
          $[alpha_j-varepsilon_0,alpha_j]$ and $[alpha_j,alpha_j+varepsilon_0]$.
          Then we have
          $$
          g(alpha_j+varepsilon)-g(alpha_j) geq varepsilon |y_j|
          quadtext{and}quad
          g(alpha_j-varepsilon)-g(alpha_j) geq varepsilon |y_j|
          $$

          for all $varepsilonin (0,varepsilon_0)$.



          Combining this with the Lipschitz constant for $h$, we can conclude
          $$
          f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j) geq varepsilon | y_j|.
          $$

          for all $varepsilonin (0,varepsilon_0)$.
          This means that
          $$
          liminf_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          geq |y_j| > 0.
          $$

          holds.
          However, this is a contradiction to $ f(alpha) = sqrt{1+alpha^2}$, because here the right-hand term is twice continuously differentiable, which (by using Taylor expansion) implies
          $$
          lim_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          =0
          $$






          share|cite|improve this answer














          Let the image of $(1,0)$ be the sequence $x=(x_i)_i$ and
          let the image of $(0,1)$ be the sequence $y=(y_i)_i$.
          Now we define the function
          For $alphainmathbb R$ we define
          $$
          f(alpha) = | x + alpha y| = sum_{i=1}^infty | x_i+alpha y_i |.
          $$

          Note that because of the isometry we should have
          $$
          f(alpha) = sqrt{1+alpha^2}.
          $$

          We cannot say that $f$ is linear, because as $alpha$ changes, there might be sign changes in some of the terms $|x_i+alpha y_i|$.



          We pick a fixed $j$ such that $y_jneq0$.
          Then we choose $Ninmathbb N$ such that
          $sum_{i=N+1}^infty |y_i| < |y_j|/2$.
          Clearly, $Ngeq j$ has to be true.
          We define
          $$
          g(alpha):=sum_{i=1}^N | x_i+alpha y_i |
          qquad
          h(alpha):=sum_{i=N+1}^infty | x_i+alpha y_i |.
          $$

          Clearly, $f=g+h$.
          It can be shown that $h(alpha)$ is Lipschitz continuous with the global Lipschitz constant $|y_j|/2$.



          Now we will analyse $g$.
          Since there are only finitely many terms, there are only finitely many points where $g$ is not smooth.
          In between those points $g$ is affine linear.
          If we consider the point $alpha_j:=-x_j/y_j$, then we can find $varepsilon_0>0$ such that
          $g$ is affine linear on the intervals
          $[alpha_j-varepsilon_0,alpha_j]$ and $[alpha_j,alpha_j+varepsilon_0]$.
          Then we have
          $$
          g(alpha_j+varepsilon)-g(alpha_j) geq varepsilon |y_j|
          quadtext{and}quad
          g(alpha_j-varepsilon)-g(alpha_j) geq varepsilon |y_j|
          $$

          for all $varepsilonin (0,varepsilon_0)$.



          Combining this with the Lipschitz constant for $h$, we can conclude
          $$
          f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j) geq varepsilon | y_j|.
          $$

          for all $varepsilonin (0,varepsilon_0)$.
          This means that
          $$
          liminf_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          geq |y_j| > 0.
          $$

          holds.
          However, this is a contradiction to $ f(alpha) = sqrt{1+alpha^2}$, because here the right-hand term is twice continuously differentiable, which (by using Taylor expansion) implies
          $$
          lim_{varepsilondownarrow 0}
          frac1varepsilon (f(alpha_j+varepsilon)+f(alpha_j-varepsilon)-2f(alpha_j))
          =0
          $$







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited Nov 29 at 11:41

























          answered Nov 29 at 10:05









          supinf

          5,9491027




          5,9491027












          • That is a good point. I think i found a fix and will update soon
            – supinf
            Nov 29 at 11:15










          • @supinf To get a contradiction you can simply use the fact that : $sum_{i = 1}^{infty} mid x_i + x cdot y_i mid$ is convex whereas $sqrt{x^2+1}-mid x_0 + x cdot y_0 mid$ is not convex ?
            – Thinking
            Nov 29 at 14:05


















          • That is a good point. I think i found a fix and will update soon
            – supinf
            Nov 29 at 11:15










          • @supinf To get a contradiction you can simply use the fact that : $sum_{i = 1}^{infty} mid x_i + x cdot y_i mid$ is convex whereas $sqrt{x^2+1}-mid x_0 + x cdot y_0 mid$ is not convex ?
            – Thinking
            Nov 29 at 14:05
















          That is a good point. I think i found a fix and will update soon
          – supinf
          Nov 29 at 11:15




          That is a good point. I think i found a fix and will update soon
          – supinf
          Nov 29 at 11:15












          @supinf To get a contradiction you can simply use the fact that : $sum_{i = 1}^{infty} mid x_i + x cdot y_i mid$ is convex whereas $sqrt{x^2+1}-mid x_0 + x cdot y_0 mid$ is not convex ?
          – Thinking
          Nov 29 at 14:05




          @supinf To get a contradiction you can simply use the fact that : $sum_{i = 1}^{infty} mid x_i + x cdot y_i mid$ is convex whereas $sqrt{x^2+1}-mid x_0 + x cdot y_0 mid$ is not convex ?
          – Thinking
          Nov 29 at 14:05











          0














          The following construction gives an approximate such imbedding. Choose an $Ngg1$, and put
          $$f(1,0)=(x_k)_{0leq kleq N-1},qquad f(0,1)=(y_k)_{0leq kleq N-1}$$
          with
          $$x_k:={piover 2N}cos{2pi kover N},quad y_k:={piover 2N}sin{2pi kover N}qquad(0leq kleq N-1) .$$
          Then, by linearity,
          $$eqalign{|f(cosphi,sinphi)|&={piover 2N}sum_{k=0}^{N-1}left|cosphicos{2kpiover N}+sinphisin{2kpiover N}right| cr &=
          {1over 4}sum_{k=0}^{N-1}{2piover N}left|cosleft(phi-{2pi kover N}right)right|cr &approx{1over4}int_{-pi}^pi|cos t|>dt=1 ,cr}$$

          independently of $phi$.



          I'm not so sure that an exact isometric imbedding is impossible.






          share|cite|improve this answer





















          • It is impossible by a convexity argument (see my comment under @supinf answer). Nevertheless this a nice approximation how did you get that idea ?
            – Thinking
            Nov 29 at 15:06
















          0














          The following construction gives an approximate such imbedding. Choose an $Ngg1$, and put
          $$f(1,0)=(x_k)_{0leq kleq N-1},qquad f(0,1)=(y_k)_{0leq kleq N-1}$$
          with
          $$x_k:={piover 2N}cos{2pi kover N},quad y_k:={piover 2N}sin{2pi kover N}qquad(0leq kleq N-1) .$$
          Then, by linearity,
          $$eqalign{|f(cosphi,sinphi)|&={piover 2N}sum_{k=0}^{N-1}left|cosphicos{2kpiover N}+sinphisin{2kpiover N}right| cr &=
          {1over 4}sum_{k=0}^{N-1}{2piover N}left|cosleft(phi-{2pi kover N}right)right|cr &approx{1over4}int_{-pi}^pi|cos t|>dt=1 ,cr}$$

          independently of $phi$.



          I'm not so sure that an exact isometric imbedding is impossible.






          share|cite|improve this answer





















          • It is impossible by a convexity argument (see my comment under @supinf answer). Nevertheless this a nice approximation how did you get that idea ?
            – Thinking
            Nov 29 at 15:06














          0












          0








          0






          The following construction gives an approximate such imbedding. Choose an $Ngg1$, and put
          $$f(1,0)=(x_k)_{0leq kleq N-1},qquad f(0,1)=(y_k)_{0leq kleq N-1}$$
          with
          $$x_k:={piover 2N}cos{2pi kover N},quad y_k:={piover 2N}sin{2pi kover N}qquad(0leq kleq N-1) .$$
          Then, by linearity,
          $$eqalign{|f(cosphi,sinphi)|&={piover 2N}sum_{k=0}^{N-1}left|cosphicos{2kpiover N}+sinphisin{2kpiover N}right| cr &=
          {1over 4}sum_{k=0}^{N-1}{2piover N}left|cosleft(phi-{2pi kover N}right)right|cr &approx{1over4}int_{-pi}^pi|cos t|>dt=1 ,cr}$$

          independently of $phi$.



          I'm not so sure that an exact isometric imbedding is impossible.






          share|cite|improve this answer












          The following construction gives an approximate such imbedding. Choose an $Ngg1$, and put
          $$f(1,0)=(x_k)_{0leq kleq N-1},qquad f(0,1)=(y_k)_{0leq kleq N-1}$$
          with
          $$x_k:={piover 2N}cos{2pi kover N},quad y_k:={piover 2N}sin{2pi kover N}qquad(0leq kleq N-1) .$$
          Then, by linearity,
          $$eqalign{|f(cosphi,sinphi)|&={piover 2N}sum_{k=0}^{N-1}left|cosphicos{2kpiover N}+sinphisin{2kpiover N}right| cr &=
          {1over 4}sum_{k=0}^{N-1}{2piover N}left|cosleft(phi-{2pi kover N}right)right|cr &approx{1over4}int_{-pi}^pi|cos t|>dt=1 ,cr}$$

          independently of $phi$.



          I'm not so sure that an exact isometric imbedding is impossible.







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered Nov 29 at 10:39









          Christian Blatter

          172k7112325




          172k7112325












          • It is impossible by a convexity argument (see my comment under @supinf answer). Nevertheless this a nice approximation how did you get that idea ?
            – Thinking
            Nov 29 at 15:06


















          • It is impossible by a convexity argument (see my comment under @supinf answer). Nevertheless this a nice approximation how did you get that idea ?
            – Thinking
            Nov 29 at 15:06
















          It is impossible by a convexity argument (see my comment under @supinf answer). Nevertheless this a nice approximation how did you get that idea ?
          – Thinking
          Nov 29 at 15:06




          It is impossible by a convexity argument (see my comment under @supinf answer). Nevertheless this a nice approximation how did you get that idea ?
          – Thinking
          Nov 29 at 15:06


















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