Why taking integral of both sides of matrix inequality is allowed?












1












$begingroup$


How to show if $nabla^2 f(x) succeq alpha I$, then the function is $alpha$-strongly convex?



In my optimization notes I have
$$nabla^2 f(x) succeq alpha I rightarrow alphatext{-strongly convex} ,,,,,,forall x$$
where $x in mathbb{R}^n$ and $Asucceq B$ means $A-B$ is positive semi-definite.



For the proof we use mean value theorem
$$
nabla f(y)-nabla f(y) = int_0^1 nabla^2 f(x+t(y-x))(y-x)dt
$$

where $x, y in mathbb{R}^n$ and $0 leq t leq 1$.
$$
langle nabla f(y)-nabla f(y) ,y-x rangle= langle int_0^1 nabla^2 f(x+t(y-x))(y-x)dt,y-x rangle
$$

Since
$$nabla^2 f(x+t(y-x)) succeq alpha I tag{1}$$
and the fact that $langle Ad,d rangle geq c|d|^2 leftrightarrow A succeq cI tag{2}$



$$
langle int_0^1 nabla^2 f(x+t(y-x))(y-x)dt,y-x rangle geq alpha |y-x|^2 tag{3}
$$

I do not understand why how we can use $(1)$ to get $(2)$? Because I do not know why we can use $(1)$ and write
$$
A=int_0^1 nabla^2 f(x+t(y-x))dt succeq int_0^1 alpha I dt= alpha I
$$

and then use $(2)$.



Is it possible to take integral from any matrix inequality?










share|cite|improve this question









$endgroup$

















    1












    $begingroup$


    How to show if $nabla^2 f(x) succeq alpha I$, then the function is $alpha$-strongly convex?



    In my optimization notes I have
    $$nabla^2 f(x) succeq alpha I rightarrow alphatext{-strongly convex} ,,,,,,forall x$$
    where $x in mathbb{R}^n$ and $Asucceq B$ means $A-B$ is positive semi-definite.



    For the proof we use mean value theorem
    $$
    nabla f(y)-nabla f(y) = int_0^1 nabla^2 f(x+t(y-x))(y-x)dt
    $$

    where $x, y in mathbb{R}^n$ and $0 leq t leq 1$.
    $$
    langle nabla f(y)-nabla f(y) ,y-x rangle= langle int_0^1 nabla^2 f(x+t(y-x))(y-x)dt,y-x rangle
    $$

    Since
    $$nabla^2 f(x+t(y-x)) succeq alpha I tag{1}$$
    and the fact that $langle Ad,d rangle geq c|d|^2 leftrightarrow A succeq cI tag{2}$



    $$
    langle int_0^1 nabla^2 f(x+t(y-x))(y-x)dt,y-x rangle geq alpha |y-x|^2 tag{3}
    $$

    I do not understand why how we can use $(1)$ to get $(2)$? Because I do not know why we can use $(1)$ and write
    $$
    A=int_0^1 nabla^2 f(x+t(y-x))dt succeq int_0^1 alpha I dt= alpha I
    $$

    and then use $(2)$.



    Is it possible to take integral from any matrix inequality?










    share|cite|improve this question









    $endgroup$















      1












      1








      1





      $begingroup$


      How to show if $nabla^2 f(x) succeq alpha I$, then the function is $alpha$-strongly convex?



      In my optimization notes I have
      $$nabla^2 f(x) succeq alpha I rightarrow alphatext{-strongly convex} ,,,,,,forall x$$
      where $x in mathbb{R}^n$ and $Asucceq B$ means $A-B$ is positive semi-definite.



      For the proof we use mean value theorem
      $$
      nabla f(y)-nabla f(y) = int_0^1 nabla^2 f(x+t(y-x))(y-x)dt
      $$

      where $x, y in mathbb{R}^n$ and $0 leq t leq 1$.
      $$
      langle nabla f(y)-nabla f(y) ,y-x rangle= langle int_0^1 nabla^2 f(x+t(y-x))(y-x)dt,y-x rangle
      $$

      Since
      $$nabla^2 f(x+t(y-x)) succeq alpha I tag{1}$$
      and the fact that $langle Ad,d rangle geq c|d|^2 leftrightarrow A succeq cI tag{2}$



      $$
      langle int_0^1 nabla^2 f(x+t(y-x))(y-x)dt,y-x rangle geq alpha |y-x|^2 tag{3}
      $$

      I do not understand why how we can use $(1)$ to get $(2)$? Because I do not know why we can use $(1)$ and write
      $$
      A=int_0^1 nabla^2 f(x+t(y-x))dt succeq int_0^1 alpha I dt= alpha I
      $$

      and then use $(2)$.



      Is it possible to take integral from any matrix inequality?










      share|cite|improve this question









      $endgroup$




      How to show if $nabla^2 f(x) succeq alpha I$, then the function is $alpha$-strongly convex?



      In my optimization notes I have
      $$nabla^2 f(x) succeq alpha I rightarrow alphatext{-strongly convex} ,,,,,,forall x$$
      where $x in mathbb{R}^n$ and $Asucceq B$ means $A-B$ is positive semi-definite.



      For the proof we use mean value theorem
      $$
      nabla f(y)-nabla f(y) = int_0^1 nabla^2 f(x+t(y-x))(y-x)dt
      $$

      where $x, y in mathbb{R}^n$ and $0 leq t leq 1$.
      $$
      langle nabla f(y)-nabla f(y) ,y-x rangle= langle int_0^1 nabla^2 f(x+t(y-x))(y-x)dt,y-x rangle
      $$

      Since
      $$nabla^2 f(x+t(y-x)) succeq alpha I tag{1}$$
      and the fact that $langle Ad,d rangle geq c|d|^2 leftrightarrow A succeq cI tag{2}$



      $$
      langle int_0^1 nabla^2 f(x+t(y-x))(y-x)dt,y-x rangle geq alpha |y-x|^2 tag{3}
      $$

      I do not understand why how we can use $(1)$ to get $(2)$? Because I do not know why we can use $(1)$ and write
      $$
      A=int_0^1 nabla^2 f(x+t(y-x))dt succeq int_0^1 alpha I dt= alpha I
      $$

      and then use $(2)$.



      Is it possible to take integral from any matrix inequality?







      integration matrices positive-semidefinite






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      asked Jan 17 at 19:24









      SaeedSaeed

      1,149310




      1,149310






















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          $begingroup$

          In your statement of the "mean value theorem" and in other parts of your post, you are taking the integral of a vector or a matrix, which does not make sense.



          I think it would be better to state the integral equality as
          $$langle nabla f(y) - nabla f(x), y-x rangle
          = int_0^1 langle nabla^2 f(x + t(y-x)) (y-x), y-x rangle , dt.$$

          (This is simply the fundamental theorem of calculus $g(1) - g(0) = int_0^1 g'(t) , dt$ applied to $g(t) := langle nabla f(x + t(y-x)), y-x rangle$.)



          From here, you can immediately use
          $nabla^2 f(x + t(y-x)) (y-x), y-x rangle ge alpha |y-x|^2$ from (2), as desired.






          share|cite|improve this answer









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            $begingroup$

            In your statement of the "mean value theorem" and in other parts of your post, you are taking the integral of a vector or a matrix, which does not make sense.



            I think it would be better to state the integral equality as
            $$langle nabla f(y) - nabla f(x), y-x rangle
            = int_0^1 langle nabla^2 f(x + t(y-x)) (y-x), y-x rangle , dt.$$

            (This is simply the fundamental theorem of calculus $g(1) - g(0) = int_0^1 g'(t) , dt$ applied to $g(t) := langle nabla f(x + t(y-x)), y-x rangle$.)



            From here, you can immediately use
            $nabla^2 f(x + t(y-x)) (y-x), y-x rangle ge alpha |y-x|^2$ from (2), as desired.






            share|cite|improve this answer









            $endgroup$


















              2












              $begingroup$

              In your statement of the "mean value theorem" and in other parts of your post, you are taking the integral of a vector or a matrix, which does not make sense.



              I think it would be better to state the integral equality as
              $$langle nabla f(y) - nabla f(x), y-x rangle
              = int_0^1 langle nabla^2 f(x + t(y-x)) (y-x), y-x rangle , dt.$$

              (This is simply the fundamental theorem of calculus $g(1) - g(0) = int_0^1 g'(t) , dt$ applied to $g(t) := langle nabla f(x + t(y-x)), y-x rangle$.)



              From here, you can immediately use
              $nabla^2 f(x + t(y-x)) (y-x), y-x rangle ge alpha |y-x|^2$ from (2), as desired.






              share|cite|improve this answer









              $endgroup$
















                2












                2








                2





                $begingroup$

                In your statement of the "mean value theorem" and in other parts of your post, you are taking the integral of a vector or a matrix, which does not make sense.



                I think it would be better to state the integral equality as
                $$langle nabla f(y) - nabla f(x), y-x rangle
                = int_0^1 langle nabla^2 f(x + t(y-x)) (y-x), y-x rangle , dt.$$

                (This is simply the fundamental theorem of calculus $g(1) - g(0) = int_0^1 g'(t) , dt$ applied to $g(t) := langle nabla f(x + t(y-x)), y-x rangle$.)



                From here, you can immediately use
                $nabla^2 f(x + t(y-x)) (y-x), y-x rangle ge alpha |y-x|^2$ from (2), as desired.






                share|cite|improve this answer









                $endgroup$



                In your statement of the "mean value theorem" and in other parts of your post, you are taking the integral of a vector or a matrix, which does not make sense.



                I think it would be better to state the integral equality as
                $$langle nabla f(y) - nabla f(x), y-x rangle
                = int_0^1 langle nabla^2 f(x + t(y-x)) (y-x), y-x rangle , dt.$$

                (This is simply the fundamental theorem of calculus $g(1) - g(0) = int_0^1 g'(t) , dt$ applied to $g(t) := langle nabla f(x + t(y-x)), y-x rangle$.)



                From here, you can immediately use
                $nabla^2 f(x + t(y-x)) (y-x), y-x rangle ge alpha |y-x|^2$ from (2), as desired.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Jan 17 at 19:39









                angryavianangryavian

                42.6k23481




                42.6k23481






























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