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Imagine we are interested in the following problem:

$$ \min_{w} \left( w^T V w + \lambda \|w\| \right) \\ \text{s.t. } w^T R \geq c $$

Where 𝑤 is an N x 1 vector, V is an N x N covariance matrix, and 𝜆 is a regularisation parameter

Suppose further that out of these N items, however, some of them are very highly correlated (almost, but not fully, collinear).

I'm not interested in obtaining the unique best solution here, but instead in ways to extract multiple "K" (ideally sparse) near optimal solution vectors. What are ways to go about this problem?

Have thought about random random perturbations (or partitions) to the V matrix, but I was wondering if someone can help think of less 'empirical' approaches. Could eigen-decomposition be used to assist in extracting orthogonal solutions?

Thanks!

Imagine we are interested in the following problem:

$$ \min_{w} \left( w^T V w + \lambda \|w\| \right) \\ \text{s.t. } w^T R \geq c $$

Where 𝑤 is an $N \times 1$ vector, $V$ is an $N \times N$ covariance matrix, and 𝜆 is a regularisation parameter

Suppose further that out of these $N$ items, however, some of them are very highly correlated (almost, but not fully, collinear).

I'm not interested in obtaining the unique best solution here, but instead in ways to extract multiple "$K$" (ideally sparse) near optimal solution vectors. What are ways to go about this problem?

Have thought about random random perturbations (or partitions) to the $V$ matrix, but I was wondering if someone can help think of less 'empirical' approaches. Could eigen-decomposition be used to assist in extracting orthogonal solutions?

Thanks!

Imagine we are interested in the following problem:

$$ \min_{w} \left( w^T V w + \lambda \|w\| \right) $$

(Edit) St w’ R > c

Where 𝑤 is an N x 1 vector, V is an N x N covariance matrix, and 𝜆 is a regularisation parameter

Suppose further that out of these N items, however, some of them are very highly correlated (almost, but not fully, collinear).

I'm not interested in obtaining the unique best solution here, but instead in ways to extract multiple "K" (ideally sparse) near optimal solution vectors. What are ways to go about this problem?

Have thought about random random perturbations (or partitions) to the V matrix, but I was wondering if someone can help think of less 'empirical' approaches. Could eigen-decomposition be used to assist in extracting orthogonal solutions?

Thanks!

Imagine we are interested in the following problem:

$$ \min_{w} \left( w^T V w + \lambda \|w\| \right) \\ \text{s.t. } w^T R \geq c $$

Where 𝑤 is an N x 1 vector, V is an N x N covariance matrix, and 𝜆 is a regularisation parameter

Suppose further that out of these N items, however, some of them are very highly correlated (almost, but not fully, collinear).

I'm not interested in obtaining the unique best solution here, but instead in ways to extract multiple "K" (ideally sparse) near optimal solution vectors. What are ways to go about this problem?

Have thought about random random perturbations (or partitions) to the V matrix, but I was wondering if someone can help think of less 'empirical' approaches. Could eigen-decomposition be used to assist in extracting orthogonal solutions?

Thanks!

Imagine we are interested in the following problem:

$$ \min_{w} \left( w^T V w + \lambda \|w\| \right) $$

Where 𝑤 is an N x 1 vector, V is an N x N covariance matrix, and 𝜆 is a regularisation parameter

Suppose further that out of these N items, however, some of them are very highly correlated (almost, but not fully, collinear).

I'm not interested in obtaining the unique best solution here, but instead in ways to extract multiple "K" (ideally sparse) near optimal solution vectors. What are ways to go about this problem?

Have thought about random random perturbations (or partitions) to the V matrix, but I was wondering if someone can help think of less 'empirical' approaches. Could eigen-decomposition be used to assist in extracting orthogonal solutions?

Thanks!

Imagine we are interested in the following problem:

$$ \min_{w} \left( w^T V w + \lambda \|w\| \right) $$

(Edit) St w’ R > c

Where 𝑤 is an N x 1 vector, V is an N x N covariance matrix, and 𝜆 is a regularisation parameter

Suppose further that out of these N items, however, some of them are very highly correlated (almost, but not fully, collinear).

I'm not interested in obtaining the unique best solution here, but instead in ways to extract multiple "K" (ideally sparse) near optimal solution vectors. What are ways to go about this problem?

Have thought about random random perturbations (or partitions) to the V matrix, but I was wondering if someone can help think of less 'empirical' approaches. Could eigen-decomposition be used to assist in extracting orthogonal solutions?

Thanks!