Hi all,

I recently encounter a difficult problem.

I wish to minimize in $ \mathbf{x} $ the sum $\min \sum_{i=1..n} (\mathbf{x}^T \mathbf{A}_i \mathbf{x})^2$ given the constraints on the norms of **all** $\mathbf{x}$'s subcomponents (let's say three 3-by-1 vectors) $|\mathbf{x}_1| = 1, |\mathbf{x}_2| = 1, |\mathbf{x}_3| = 1$. $\mathbf{A}_i$ may not be positive-definite.

Yes, it's quartic expression that we want to minimize. I'm not sure if any one has worked on this or similar problem in the math community. I search the literature for sometimes but no use. My question may be similar but actually much more difficult than this Least square given constraint on subcomponents

The 4th-order and constraints on **all** subcomponents makes it really hard for me to handle.

Any idea to a numerical/analytical solution, is greatly appreciated. Thanks for reading.

p/s: $\mathbf{x} = [\mathbf{x}_1^T , \mathbf{x}_2^T, \mathbf{x}_3^T]^T$. By "subcomponents", I mean the subvectors, as shown in the equation.