How to solver the least square problem involves with the variable is the product of Hadamard Product?

I encounted an least square problem involves hadamard product of two $100\times 1$ matrix: X, Y: $$ A_{N\times100}*(X_{100\times 1}∘Y_{100\times 1})_{100\times 1} = B_{N\times 1} $$ In above equation, * is a traditional matrix multiplication, ∘is the hadamard product. A and B are observed known matrix， X is also known . I known this is non-convex problem. My question: is there any method to get an approximate anser of Y?

Let $\mathbf{y}=\mathbf{x} \circ \mathbf{x}$, a 100-by-1 vector with positive entries. The least-squares problem you are facing is now recast into a non-negative least-squares (NNLS) , i.e. minimize $$ \phi(\mathbf{y})=\| \mathbf{Ay-b} \|^2, s.t. \mathbf{y} \ge 0 $$ Once $\mathbf{y}$ is found, take the (elementwise) square root to obtain $\mathbf{x}$

Let $\mathbf{z}=\mathbf{x} \circ \mathbf{y}$, Since $\mathbf{x}$ is known, the method consists in 1) solving for $\mathbf{z}$ using standard LS and then 2) retrieve $\mathbf{y}$ by elementwise division : $$ \mathbf{y}=\frac{\mathbf{z}_{LS}}{\mathbf{x}} $$