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Definition. Let $u:\Omega \rightarrow \mathbb{R} $.

  A function $u$ is called semiconvexsemiconvex if $u=v+w$ for some $v\in C^{1,1}(\Omega)$ and a convex function $w$ (it's equivalent saying.

Note. Saying that $u$ is semiconvex ifis equivalent to say that there exists a $\lambda$ such that the function $z(x)=u(x)+\dfrac{|x|^2}{2\lambda}$ is convex). $$ z(x)=u(x)+\dfrac{|x|^2}{2\lambda}\text{ is convex}.$$

Consider the elliptic operator of the form $$Lu=a^{ij}D_{ij}u+b^iD_iu$$ and let $L$ be uniformly elliptic.

I want to showprove the following statement:

Theorem  (Aleksandrov maximum principle): Let $u$ be semiconvex in $\Omega$ and suppose $Lu+f\geq0$ almost everywhere in $\Omega$ for some $f\in L^{n}(\Omega)$. We then have the following estimates: $$ \sup_{\Omega}u \leq \sup_{\partial\Omega}u+ C ||f||_{L^n(\Gamma^+)}$$$$ \sup_{\Omega}u \leq \sup_{\partial\Omega}u+ C \Vert f\Vert_{L^n(\Gamma^+)}$$

where $\Gamma^+$ is upper contact set of $u$ ( aa sub domain of $\Omega$ where the Hessian of $u$ is negative define).

I know that this result holds for subsolutionsubsolutions $u\in W^{2,n}(\Omega)$, an extension through molltification ofas it can be shown by extending the same result for the case $u\in C^2(\Omega)$ through mollification. So I thought that I can deduceddeduce the validity of my Aleksandrov maximum principle from its validity for classical subsolution, by mollification or something like this.

  Could this be true? Can somebody please help me?

Let $u:\Omega \rightarrow \mathbb{R} $.

  A function $u$ is called semiconvex if $u=v+w$ for some $v\in C^{1,1}(\Omega)$ and convex function $w$ (it's equivalent saying that $u$ is semiconvex if exists a $\lambda$ such that the function $z(x)=u(x)+\dfrac{|x|^2}{2\lambda}$ is convex).

Consider the elliptic operator of the form $$Lu=a^{ij}D_{ij}u+b^iD_iu$$ and let $L$ be uniformly elliptic.

I want to show the following statement:

Theorem(Aleksandrov maximum principle): Let $u$ be semiconvex in $\Omega$ and suppose $Lu+f\geq0$ almost everywhere in $\Omega$ for some $f\in L^{n}(\Omega)$. We then have the following estimates: $$ \sup_{\Omega}u \leq \sup_{\partial\Omega}u+ C ||f||_{L^n(\Gamma^+)}$$

where $\Gamma^+$ is upper contact set of $u$ ( a sub domain of $\Omega$ where the Hessian of $u$ is negative define).

I know this result for subsolution $u\in W^{2,n}(\Omega)$, an extension through molltification of the case $u\in C^2(\Omega)$. So I thought that I can deduced the validity of my Aleksandrov maximum principle from its validity for classical subsolution, by mollification or something like this.

  Can somebody please help me?

Definition. Let $u:\Omega \rightarrow \mathbb{R} $. A function $u$ is called semiconvex if $u=v+w$ for some $v\in C^{1,1}(\Omega)$ and a convex function $w$.

Note. Saying that $u$ is semiconvex is equivalent to say that there exists a $\lambda$ such that the function $$ z(x)=u(x)+\dfrac{|x|^2}{2\lambda}\text{ is convex}.$$

Consider the elliptic operator of the form $$Lu=a^{ij}D_{ij}u+b^iD_iu$$ and let $L$ be uniformly elliptic.

I want to prove the following statement:

Theorem  (Aleksandrov maximum principle): Let $u$ be semiconvex in $\Omega$ and suppose $Lu+f\geq0$ almost everywhere in $\Omega$ for some $f\in L^{n}(\Omega)$. We then have the following estimates: $$ \sup_{\Omega}u \leq \sup_{\partial\Omega}u+ C \Vert f\Vert_{L^n(\Gamma^+)}$$

where $\Gamma^+$ is upper contact set of $u$ (a sub domain of $\Omega$ where the Hessian of $u$ is negative define).

I know that this result holds for subsolutions $u\in W^{2,n}(\Omega)$, as it can be shown by extending the same result for the case $u\in C^2(\Omega)$ through mollification. So I thought that I can deduce the validity of my Aleksandrov maximum principle from its validity for classical subsolution, by mollification or something like this. Could this be true? Can somebody please help me?

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Aleksandrov maximum principle for semi-convex function

Let $u:\Omega \rightarrow \mathbb{R} $.

A function $u$ is called semiconvex if $u=v+w$ for some $v\in C^{1,1}(\Omega)$ and convex function $w$ (it's equivalent saying that $u$ is semiconvex if exists a $\lambda$ such that the function $z(x)=u(x)+\dfrac{|x|^2}{2\lambda}$ is convex).

Consider the elliptic operator of the form $$Lu=a^{ij}D_{ij}u+b^iD_iu$$ and let $L$ be uniformly elliptic.

I want to show the following statement:

Theorem(Aleksandrov maximum principle): Let $u$ be semiconvex in $\Omega$ and suppose $Lu+f\geq0$ almost everywhere in $\Omega$ for some $f\in L^{n}(\Omega)$. We then have the following estimates: $$ \sup_{\Omega}u \leq \sup_{\partial\Omega}u+ C ||f||_{L^n(\Gamma^+)}$$

where $\Gamma^+$ is upper contact set of $u$ ( a sub domain of $\Omega$ where the Hessian of $u$ is negative define).

I know this result for subsolution $u\in W^{2,n}(\Omega)$, an extension through molltification of the case $u\in C^2(\Omega)$. So I thought that I can deduced the validity of my Aleksandrov maximum principle from its validity for classical subsolution, by mollification or something like this.

Can somebody please help me?