Maximization with constraint

In summary, the conversation is about finding the maximum of a quadric for points on a sphere using an affine transform and determining the maximum displacement of any point on the sphere. The matrix used, B, is symmetric and the question is posed about its properties. The response is that it cannot be answered in this generality without knowing the definiteness of B.
  • #1
Highwind
6
0
Hi,

I search for the maximum of a quadric for points on a sphere.
I have an affine transform A (4x4 matrix, in homogeneous coord.) and apply it to points on (and inside) a sphere [itex] x \in S_{m,r} \Leftrightarrow (x-m)^2<=r^2 [/itex]. (Although I think the extremum must be on the surface of the sphere?).

Now I want to find the maximum displacement of any point in/on S:
[itex] d^2 (x) = (A x - x)^2 = ( (A-E) x)^2 = x^T (A-E)^T (A-E) x [/itex]

The matrix [itex] B:= (A-E)^T (A-E) [/itex] is of course symmetric.

so what is:
[itex] max_{x \in S} \ d^2(x) = max_{x \in S} \ x^T B x [/itex]

Thanks for any help...
 
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  • #2
This cannot be answered in this generality. E.g. ##B## could be negative (or positive or neither) definit, and you have basically only a symmetric bilinear form. If it is, then ##0## will be the maximum.
 

1. What is the purpose of maximization with constraint in scientific research?

Maximization with constraint is a mathematical optimization technique used in scientific research to find the best possible solution to a problem while adhering to certain constraints or limitations. It allows researchers to identify the optimal values for variables in a system while considering any restrictions or limitations that may exist.

2. How is maximization with constraint different from traditional optimization methods?

Traditional optimization methods involve finding the maximum or minimum value of a function without any constraints. In maximization with constraint, the objective function is optimized while accounting for any constraints that may be present, leading to more realistic and practical solutions.

3. Can you provide an example of a real-life application of maximization with constraint?

One common application of maximization with constraint is in production planning, where a company wants to maximize profits while adhering to resource and capacity constraints. The objective function would be to maximize profit, and the constraints would include factors such as production capacity, labor availability, and material costs.

4. What are the steps involved in solving a maximization with constraint problem?

The first step is to define the objective function and the constraints. Next, the optimization algorithm is used to find the optimal values for the variables that maximize the objective function while meeting the constraints. Finally, the results are analyzed and interpreted in the context of the problem being solved.

5. Are there any limitations to using maximization with constraint in scientific research?

While maximization with constraint is a powerful optimization technique, it does have some limitations. It may not always provide the most practical or feasible solutions, and the results can be highly sensitive to changes in the constraints. Therefore, it is essential to carefully evaluate the results and consider any potential limitations of the method being used.

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