"minimum norm solution" is correct and usable in written English.
You can use it to refer to an optimization solution that minimizes the Euclidean norm of a vector. For example, "We used a minimum norm solution to find the most efficient route."
For classification, the asymptotic convergence to the minimum norm solution implies convergence to the maximum margin solution which guarantees good classification error for "low noise" datasets.
Gradient descent enforces a form of implicit regular- ization controlled by the number of iterations, and asymptotically converging to the minimum norm solution.
Well known solutions include using the Moore-Penrose inverse, which yields a minimum norm solution; an alternative is to solve a linear system of equations.
This paper analyzes the conditions under which the minimum norm solution to this problem is non-unique, provides a physical interpretation of the issue, proposes a simple method to normalize the minimum norm solution and make it unique, and extends these principles to unilaterally constrained multibody systems.
Our Algorithm (3.2) solves the minimum norm solution of. .
Our Algorithm (3.2) solves the minimum norm solution of.
In the VESTAL approach, temporal information in the data is used to enhance the stability of the reconstructed vector-based L1-minimum norm solution.
Therefore, is the minimum-norm solution of.This completes the proof.
The sequence generated by algorithm (4.1) converges strongly to the minimum-norm solution of SFP (1.1).
Let q be the minimum-norm solution of U, that is, (q=P_{U}(0)).
Thus the minimum-norm solution for SFP (1.1) exists and is unique.
