Can the Maximum Principle be Used to Estimate Solutions of Elliptic Equations?

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In summary, the solution to the problem $-\Delta u(x)=f(x), x \in S \\ u(x)=0, x \in \partial{S}$ satisfies the estimation $|u(x)| \leq \frac{1}{4}\max_{x \in \overline{S}} |f(x)|, x \in S$ if we use the maximum principle and $Lu \ge 0$.
  • #1
mathmari
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Hey! :eek:

Let $S=\{x \in \mathbb{R}^2 \mid |x| <1\}$. Using the maximum principle I have to show that the solution of the problem $$-\Delta u(x)=f(x), x \in S \\ u(x)=0, x \in \partial{S}$$ satisfies the estimation $$|u(x)| \leq \frac{1}{4}\max_{x \in \overline{S}} |f(x)|, x \in S$$
To use the maximum principle shouldn't it stand that $$\Delta u \geq 0$$ ?? (Wondering)

Do we have to take cases for $f$, if it is positive or negative?? (Wondering)
 
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  • #2
mathmari said:
Hey! :eek:

Let $S=\{x \in \mathbb{R}^2 \mid |x| <1\}$. Using the maximum principle I have to show that the solution of the problem $$-\Delta u(x)=f(x), x \in S \\ u(x)=0, x \in \partial{S}$$ satisfies the estimation $$|u(x)| \leq \frac{1}{4}\max_{x \in \overline{S}} |f(x)|, x \in S$$
To use the maximum principle shouldn't it stand that $$\Delta u \geq 0$$ ?? (Wondering)

Do we have to take cases for $f$, if it is positive or negative?? (Wondering)

Hi! (Smile)

From wiki, to use the maximum principle we need that $Lu \ge 0$ where $L$ is an elliptic differential operator.
We could pick $Lu=\Delta x$, which satisfies the criteria.
However, since we don't know whether $\Delta u \geq 0$, we need to add or subtract something to ensure it becomes positive. (Thinking)Perhaps we should look at a 1-dimensional problem first.
Suppose we pick $S' = \{x \in \mathbb{R} \mid |x| <1\}$.

Let's define $M = \max_{x \in \overline{S'}} |f(x)|$.
What would be the solution for:
$$-\Delta u = M$$
? (Wondering)
 
  • #3
Let's rewrite your problem to:
$$\Delta u = -f(x,y) \tag 1$$
Then $\Delta u$ is an elliptic operator. (Thinking)

Let $M = \frac 14 \max |f(x,y)| \tag 2$

Let $v = u - M(1-x^2-y^2) \tag 3$

Then: $\Delta v = \Delta u + 4M = -f(x,y) + \max |f(x,y)| \ge 0\tag 4$

Now we can apply the maximum principle to $v$. (Emo)
 

1. What is the Maximum Principle-Estimation?

The Maximum Principle-Estimation is a statistical method used to estimate the parameters of a model by maximizing a likelihood function. It is based on the principle that the estimated parameters should be those that maximize the probability of obtaining the observed data.

2. How does the Maximum Principle-Estimation work?

In Maximum Principle-Estimation, the likelihood function is defined as the probability of obtaining the observed data given the model parameters. The method then uses numerical optimization techniques to find the set of parameters that maximizes this likelihood function.

3. What are the advantages of using Maximum Principle-Estimation?

One of the main advantages of Maximum Principle-Estimation is that it provides unbiased estimates of the model parameters. It also has a solid theoretical foundation and can handle complex models with a large number of parameters. Additionally, it is a flexible method that can be applied to a wide range of statistical models.

4. Are there any limitations to using Maximum Principle-Estimation?

One limitation of Maximum Principle-Estimation is that it relies on the assumption that the data has been generated from a specific statistical model. If this assumption is violated, the estimated parameters may not be accurate. Additionally, the method can be computationally intensive and may not be suitable for large datasets.

5. How is Maximum Principle-Estimation different from other estimation methods?

Maximum Principle-Estimation is a likelihood-based method, meaning that it estimates the parameters by maximizing the likelihood function. Other estimation methods, such as least squares estimation, minimize a measure of error between the observed data and the model's predictions. Maximum Principle-Estimation is also more robust to outliers compared to other methods, such as least squares.

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