In general, LGTM. Few notes:

• The name f is meaningless. newtonDelta perhaps?

• It feels right to compute a derivative's coefficients once, and reuse evalPoly for both the function and its derivative. After all, the polynomial's derivative is also polynomial. There is an usual space-time tradeoff, but in this case DRY rule casts a deciding vote.

• Newton's algorithm does not necessarily converge. You should be prepared to handle the divergent case.

• Hardcoding ε is dubious. I recommend to solveNewton have it as a parameter.

• Using Horner schedule is a definite improvement.

In general, LGTM. Few notes:

• The name f is meaningless. newtonDelta perhaps?

• It feels right to compute a derivative's coefficients once, and reuse evalPoly for both the function and its derivative. After all, the polynomial's derivative is also polynomial. There is an usual space-time tradeoff, but in the case of polynomials DRY rule casts a deciding vote.

• Newton's algorithm does not necessarily converge. You should be prepared to handle the divergent case.

• Hardcoding ε is dubious. I recommend to solveNewton have it as a parameter.

• Using Horner schedule is a definite improvement.