But some parts of math deal "with notions of 'how to'" and there commonly do so more "precisely" than computer science. There are such parts in optimization, control theory, statistics, and numerical analysis.
E.g., numerical analysis says how to (1) solve systems of linear equations numerically exactly using just single precision arithmetic, (2) solve systems of linear equations with accuracy guaranteed by error bounds from condition number, (3) solve systems of large, sparse linear equations where the matrix is large, positive definite, and diagonally dominate, (4) how to solve ordinary differential equation initial value problems, e.g., for space flight trajectories, and (5) how to solve partial differential equations of heat flow, fluid flow, and electromagnetic fields, and (6) how to do discrete Fourier transforms of positive integer n points in time proportional to n log(n) instead of n^2; statistics says how (1) to get minimum sufficient statistics in the Gaussian case, (2) how to design a most powerful statistical hypothesis test, (3) how to pick a survey sample size that will give desired accuracy, (4) how to get minimum variance, unbiased estimators in multivariate statistics, and (5) at a Web site with users arriving at a rate of 10 a second, how to find the probability of a minute with arrival rate of over 20 a second; control theory can show (1) how to execute best possible decision making over time under uncertainty, (2) how a rocket can reach orbit for minimum fuel, (3) how an airplane can climb to a given altitude in least time; and optimization can show (1) how to find least cost flows on large networks while avoiding cycling in the network simplex algorithm, (2) how to assign interceptors to targets in the best possible way in guaranteed execution time, and (3) how to assign signals to satellite channels to minimize the worst case of signal interference.