  We study practical approximations to Kolmogorov prefix complexity (K) using
IMP2, a high-level programming language. Our focus is on investigating the
interpreter optimality for this language as the reference machine for the
Coding Theorem Method (CTM). A method advanced to deal with applications to
algorithmic complexity different to the popular traditional lossless
compression approach based on the principles of algorithmic probability. The
chosen model of computation is proven to be suitable for this task and a
comparison to other models and methods is performed. Our findings show that CTM
approximations using our model do not always correlate with results from
lower-level models of computation. This suggests some models may require a
larger program space to converge to Levin's universal distribution.
Furthermore, we compare CTM with an upper bound to Kolmogorov complexity and
find a strong correlation, supporting CTM's validity as an approximation method
with finer-grade resolution of K.
