Theory And — Numerical Approximations Of Fractional Integrals And Derivatives

The choice of numerical method in fractional calculus is a trade-off between physical fidelity (long memory), computational cost (dense vs. compressed history), and regularity of the solution (smooth vs. singular at $t=0$). For many problems, the short-memory principle or sum-of-exponentials acceleration is not a luxury—it is a necessity.

$$ a^GLD^\alpha t f(t_n) \approx h^-\alpha \sum_j=0^n \omega_j^(\alpha) f(t_n-j)$$ The choice of numerical method in fractional calculus

$$ aI^\alpha t f(t) = \frac1\Gamma(\alpha) \int_a^t (t-\tau)^\alpha-1 f(\tau) , d\tau$$ computational cost (dense vs. compressed history)