What is the geometric meaning of Lagrange multipliers in SVM?

Lagrange multipliers measure how strongly each training point influences the decision boundary.

In SVM, each α (Lagrange multiplier) corresponds to a training sample. Non-zero α values indicate support vectors, meaning those points lie on or violate margin constraints and actively shape the hyperplane geometry.