Leading Principal Minors of Bordered Hessian in Constrained Max Problems

In summary, when facing a constrained maximization problem with n variables and k constraints (with k>n), there are two cases to consider. In the equality constraints case, it is necessary to check if the (n-k) leading principal minors of the bordered Hessian alternate in sign, starting from the sign given by (-1)^{n}. In the inequality constraints case, it is necessary to check if the [n-(e+k)] leading principal minors of the bordered Hessian alternate in sign, starting from the sign given by (-1)^{n}, where e is the number of binding constraints and k is the number of not-binding constraints. However, if n-k<0 or n-(e+k)<0, it is important to
  • #1
Kolmin
66
0
I am struggling a bit with the second order conditions of a constrained maximization problem with [itex]n[/itex] variables and [itex]k[/itex] constraints (with [itex]k>n[/itex]).

In the equality constraints case we have to check if the [itex](n-k)[/itex] leading principal minors of the bordered Hessian alternate in sign, starting from the sign given by [itex](-1)^{n}[/itex].

In the inequality constraints case we have to check if the [itex][n-(e+k)][/itex] leading principal minors of the bordered Hessian alternate in sign, starting from the sign given by [itex](-1)^{n}[/itex] (with [itex]e[/itex] equals to the number of binding constraints and [itex]k[/itex] equals to the number of not-binding constraints).

Fair enough, but how to I behave when I have [itex]n-k<0[/itex] or [itex]n-(e+k)<0[/itex] (e.g. 2 variables in the objective function and 4 equality constraints)?
Do I have to focus only on the number I get in order to know which minors I have to check, without focusing on the sign?


Thanks a lot. :smile:
 
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  • #2
Sorry again with my stupid questions, but could somebody give me an hint on why this problem is probably that dumb?

Is it maybe that you cannot have more constraints than variables?
But still, if the constraints equals the number of the varibales, what should I look for in the second order conditions?

Thanks. : )
 

1. What is the Bordered Hessian in constrained maximum problems?

The Bordered Hessian is a matrix that contains second-order partial derivatives of the objective function in a constrained optimization problem. It incorporates both the constraints and the objective function to determine the maximum or minimum value of the objective function subject to the given constraints.

2. What are principal minors in the Bordered Hessian?

Principal minors are the determinants of submatrices of the Bordered Hessian. These submatrices are formed by selecting a specific number of rows and columns from the Bordered Hessian matrix. The number of rows and columns selected determines the order of the principal minor.

3. How are leading principal minors determined in the Bordered Hessian?

Leading principal minors are determined by selecting the top-left submatrices of the Bordered Hessian matrix. These submatrices include the first k rows and columns, where k is the order of the leading principal minor. The leading principal minors are used to check for convexity or concavity in a constrained optimization problem.

4. What is the significance of leading principal minors in constrained maximum problems?

Leading principal minors are used to determine the nature of the objective function in a constrained optimization problem. If all the leading principal minors are positive, the objective function is convex, and the maximum value of the objective function can be found at the critical points. If all the leading principal minors are negative, the objective function is concave, and the minimum value of the objective function can be found at the critical points.

5. How are leading principal minors used to solve constrained maximum problems?

To solve a constrained maximum problem, the leading principal minors are first checked to determine the nature of the objective function. If the leading principal minors are all positive, the maximum value of the objective function can be found by setting the first-order derivatives to zero and solving the resulting equations. If the leading principal minors are all negative, the minimum value can be found in the same way. However, if the leading principal minors are a mix of positive and negative values, other methods such as the Kuhn-Tucker conditions may need to be used.

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