Problem 1
At eigenvectors; the extreme values are the smallest and largest eigenvalues.
Faculty use
Reward correct mathematics, explicit dimensions/hypotheses, interpretation, and verification. Equivalent valid reasoning should receive credit.
At eigenvectors; the extreme values are the smallest and largest eigenvalues.
Maximum stretching or maximum quadratic energy per unit norm.
Definitions should identify the vector/matrix/space/transformation, dimensions or domain/codomain, and the hypotheses behind finding extreme quadratic values on the unit sphere. Use the formulas R_A(x)=xᵀAx/xᵀx; min λ≤R_A(x)≤max λ.
A strong error analysis identifies the first step that violates a definition, dimension, theorem hypothesis, or matrix-operation rule; then repairs that step and re-verifies the final result.
Application responses vary. Require defined variables, a compatible matrix/vector model connected to energy models, covariance, optimization, conic classification, stability, and mechanics, a complete computation, interpretation, and limitations.
Accept two independent checks appropriate to the task: reconstruction, residual, RREF comparison, determinant/rank test, orthogonality, substitution, eigenpair check Av=λv, factorization product, or a second method.
Six problems × 10 points: 5 mathematical accuracy, 2 structure/justification, 2 verification/interpretation, 1 notation and communication.