Final answer:
The condition number of a matrix measures how sensitive the solution is to small errors. A high condition number indicates a more ill-conditioned matrix while a low condition number indicates a more well-conditioned matrix.
Step-by-step explanation:
The condition number of a matrix A is a measure of how sensitive the solution of the linear system Ax = b is to small errors in the data of A and b. It is denoted by ||A|| * ||A^-1||, where ||A|| denotes the norm of A and ||A^-1|| denotes the norm of the inverse of A.
If the condition number of A is large, small errors in A and b can result in large errors in the solution of Ax. If the condition number is small, small errors in A and b will result in small errors in the solution of Ax.
When comparing matrices based on their condition numbers, a lower condition number indicates a more well-conditioned matrix. This means that small errors in the data will have less impact on the accuracy of the solution. Conversely, a higher condition number indicates a more ill-conditioned matrix, where small errors can lead to large errors in the solution.
The relationship between errors and solutions is such that small errors in the data of a matrix A can propagate and amplify in the solution of Ax. This amplification is determined by the condition number of A.