Faculty use

Kernel, Image & Rank–Nullity for Transformations

Reward correct mathematics, explicit dimensions/hypotheses, interpretation, and verification. Equivalent valid reasoning should receive credit.

Problem 1

x=−y,z=−y, so kerT=span{(−1,1,−1)}.

Problem 2

The standard matrix has rank 2, so dim imT=2.

Problem 3

Definitions should identify the vector/matrix/space/transformation, dimensions or domain/codomain, and the hypotheses behind measuring collapsed inputs and attainable outputs. Use the formulas kerT={v:T(v)=0}; imT={T(v):v∈V}.

Problem 4

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.

Problem 5

Application responses vary. Require defined variables, a compatible matrix/vector model connected to graphics, coordinate changes, differential operators, data pipelines, and reversible models, a complete computation, interpretation, and limitations.

Problem 6

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.

Scoring guidance

Six problems × 10 points: 5 mathematical accuracy, 2 structure/justification, 2 verification/interpretation, 1 notation and communication.

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