Optimization in Banach space: Find functions that minimize the supremum of a convex operator.












2












$begingroup$


Let $D subset mathbb{R}^n$ be compact. Denote by $C(D, mathbb{R}^n)$ the space of continuous functions from $D$ to $mathbb{R}^n$.



Let $K$ be a real, symmetric, positive-definite $n times n$ matrix and let $k in C(D, mathbb{R}^n)$.



We consider the following convex "functional":
$$mathcal{F}:C(D, mathbb{R}^n) rightarrow C(D
)$$

$$mathcal{F}[b](x) := b(x)^T K b(x) - 2 k(x)^T b(x)$$
where $^T$ denotes transpose.



We are interested in the quantity
$$M_b := sup_{x in D} mathcal{F}[b](x)$$



We are looking for the function $b in C(D, mathbb{R}^n)$ that minimizes this quantity, under the constraint that $b(x)$ lies on the probability simplex $Delta$ for all $x$.
$$inf_{substack{b in C(D, mathbb{R}^n)} \ b in Delta} ~ ~ Big(sup_{x in D} mathcal{F}[b](x)Big)$$



Formally, the constraint $b in Delta$ is:
$$forall x in D: sum_{i = 1}^n b_i(x) = 1 text{ and } b_i(x) geq 0 text{ for i=1, ...,n} $$
Where, for $b in C(D, mathbb{R}^n)$, we write: $b(x) = begin{pmatrix}
d_1(x)\
vdots\
d_n(x)
end{pmatrix}$



I am looking for any reference that could help me solve this problem.
More precisely, references to any article / book related to the subject would already help. I don't even know that the name of this field of mathematics is.



Remarks:




  • I am interested in numerical or analytical solutions to the above problem.

  • An approximate solution would also be OK.

  • I know the solution to the unconstrained version of the problem.

  • The current setting is to look for solutions in the class of continuous functions, but that setting can be relaxed: in the end, piecewise continous is enough.










share|cite|improve this question









$endgroup$

















    2












    $begingroup$


    Let $D subset mathbb{R}^n$ be compact. Denote by $C(D, mathbb{R}^n)$ the space of continuous functions from $D$ to $mathbb{R}^n$.



    Let $K$ be a real, symmetric, positive-definite $n times n$ matrix and let $k in C(D, mathbb{R}^n)$.



    We consider the following convex "functional":
    $$mathcal{F}:C(D, mathbb{R}^n) rightarrow C(D
    )$$

    $$mathcal{F}[b](x) := b(x)^T K b(x) - 2 k(x)^T b(x)$$
    where $^T$ denotes transpose.



    We are interested in the quantity
    $$M_b := sup_{x in D} mathcal{F}[b](x)$$



    We are looking for the function $b in C(D, mathbb{R}^n)$ that minimizes this quantity, under the constraint that $b(x)$ lies on the probability simplex $Delta$ for all $x$.
    $$inf_{substack{b in C(D, mathbb{R}^n)} \ b in Delta} ~ ~ Big(sup_{x in D} mathcal{F}[b](x)Big)$$



    Formally, the constraint $b in Delta$ is:
    $$forall x in D: sum_{i = 1}^n b_i(x) = 1 text{ and } b_i(x) geq 0 text{ for i=1, ...,n} $$
    Where, for $b in C(D, mathbb{R}^n)$, we write: $b(x) = begin{pmatrix}
    d_1(x)\
    vdots\
    d_n(x)
    end{pmatrix}$



    I am looking for any reference that could help me solve this problem.
    More precisely, references to any article / book related to the subject would already help. I don't even know that the name of this field of mathematics is.



    Remarks:




    • I am interested in numerical or analytical solutions to the above problem.

    • An approximate solution would also be OK.

    • I know the solution to the unconstrained version of the problem.

    • The current setting is to look for solutions in the class of continuous functions, but that setting can be relaxed: in the end, piecewise continous is enough.










    share|cite|improve this question









    $endgroup$















      2












      2








      2


      1



      $begingroup$


      Let $D subset mathbb{R}^n$ be compact. Denote by $C(D, mathbb{R}^n)$ the space of continuous functions from $D$ to $mathbb{R}^n$.



      Let $K$ be a real, symmetric, positive-definite $n times n$ matrix and let $k in C(D, mathbb{R}^n)$.



      We consider the following convex "functional":
      $$mathcal{F}:C(D, mathbb{R}^n) rightarrow C(D
      )$$

      $$mathcal{F}[b](x) := b(x)^T K b(x) - 2 k(x)^T b(x)$$
      where $^T$ denotes transpose.



      We are interested in the quantity
      $$M_b := sup_{x in D} mathcal{F}[b](x)$$



      We are looking for the function $b in C(D, mathbb{R}^n)$ that minimizes this quantity, under the constraint that $b(x)$ lies on the probability simplex $Delta$ for all $x$.
      $$inf_{substack{b in C(D, mathbb{R}^n)} \ b in Delta} ~ ~ Big(sup_{x in D} mathcal{F}[b](x)Big)$$



      Formally, the constraint $b in Delta$ is:
      $$forall x in D: sum_{i = 1}^n b_i(x) = 1 text{ and } b_i(x) geq 0 text{ for i=1, ...,n} $$
      Where, for $b in C(D, mathbb{R}^n)$, we write: $b(x) = begin{pmatrix}
      d_1(x)\
      vdots\
      d_n(x)
      end{pmatrix}$



      I am looking for any reference that could help me solve this problem.
      More precisely, references to any article / book related to the subject would already help. I don't even know that the name of this field of mathematics is.



      Remarks:




      • I am interested in numerical or analytical solutions to the above problem.

      • An approximate solution would also be OK.

      • I know the solution to the unconstrained version of the problem.

      • The current setting is to look for solutions in the class of continuous functions, but that setting can be relaxed: in the end, piecewise continous is enough.










      share|cite|improve this question









      $endgroup$




      Let $D subset mathbb{R}^n$ be compact. Denote by $C(D, mathbb{R}^n)$ the space of continuous functions from $D$ to $mathbb{R}^n$.



      Let $K$ be a real, symmetric, positive-definite $n times n$ matrix and let $k in C(D, mathbb{R}^n)$.



      We consider the following convex "functional":
      $$mathcal{F}:C(D, mathbb{R}^n) rightarrow C(D
      )$$

      $$mathcal{F}[b](x) := b(x)^T K b(x) - 2 k(x)^T b(x)$$
      where $^T$ denotes transpose.



      We are interested in the quantity
      $$M_b := sup_{x in D} mathcal{F}[b](x)$$



      We are looking for the function $b in C(D, mathbb{R}^n)$ that minimizes this quantity, under the constraint that $b(x)$ lies on the probability simplex $Delta$ for all $x$.
      $$inf_{substack{b in C(D, mathbb{R}^n)} \ b in Delta} ~ ~ Big(sup_{x in D} mathcal{F}[b](x)Big)$$



      Formally, the constraint $b in Delta$ is:
      $$forall x in D: sum_{i = 1}^n b_i(x) = 1 text{ and } b_i(x) geq 0 text{ for i=1, ...,n} $$
      Where, for $b in C(D, mathbb{R}^n)$, we write: $b(x) = begin{pmatrix}
      d_1(x)\
      vdots\
      d_n(x)
      end{pmatrix}$



      I am looking for any reference that could help me solve this problem.
      More precisely, references to any article / book related to the subject would already help. I don't even know that the name of this field of mathematics is.



      Remarks:




      • I am interested in numerical or analytical solutions to the above problem.

      • An approximate solution would also be OK.

      • I know the solution to the unconstrained version of the problem.

      • The current setting is to look for solutions in the class of continuous functions, but that setting can be relaxed: in the end, piecewise continous is enough.







      optimization banach-spaces calculus-of-variations variational-analysis






      share|cite|improve this question













      share|cite|improve this question











      share|cite|improve this question




      share|cite|improve this question










      asked Nov 29 '18 at 10:30









      Cédric TravellettiCédric Travelletti

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