Fitting trajectories in Video S5 show many examples of stalling and abnormal convergence corresponding to panels (E) and (F) in Figure . Stalling occurs when a single fitting iteration, ending with |𝚫𝚫𝐩𝐩| ≈ 0, starts an insidious cycle where 𝐩𝐩 doesn't change and so the algorithm is doomed to repeat itself barring some intervention. This is distinct from local or global minima because stalling also requires |∇C| � > 10 -9 , which implies 𝐩𝐩 is not a minimum of C(𝐩𝐩). Two scenarios for which stalling may occur are (1) 𝚫𝚫𝐩𝐩 is accurate in direction but |𝚫𝚫𝐩𝐩| is limited by a boundary condition which causes |𝚫𝚫𝐩𝐩| ≈ 0 or (2) multicollinearity among fitting parameters leads to a nearly singular 𝛁𝛁𝛁𝛁𝛁𝛁 which results in an erroneous vector direction of 𝚫𝚫𝐩𝐩 (Eq. 6) and hence, 𝚫𝚫𝐩𝐩 is unable to reduce the cost function and so |𝚫𝚫𝐩𝐩| ≈ 0. Figure illustrates three such examples of stalling. For each example, the top panel (A), (C), or (E) shows fitting trajectories of Kubo amplitudes (y-axis) versus time constants (x-axis) where the dashed line reticles mark the "true" value from the simulation input, and the bottom panel (B), (D), or (F) shows the plot of SIGN versus iteration for the fit. The first example in the left panel of Figure shows the case of boundary stalling, in which the direction 𝚫𝚫𝐩𝐩 is accurate for further minimizing the cost function, but the location 𝐩𝐩 + 𝚫𝚫𝐩𝐩 is outside of the user-defined boundary for at least one of the parameters (in this case τ c ≤ 10). Convergence (|∇C| � ≪ 1) is impossible in the case of regular boundary stalling and the program readily detects the constant value of |∇C| � and resolves the stall with a random restart.