Diagnosis can be mistaken primarily with SPG2, also caused by mutations on the PLP1 gene, differing on signs such as autonomic dysfunction and characteristic paraplegia.
PLP1/DM20 exact way of functioning has not been described precisely to date; however, it is clear they are needed for assembly and stability of the myelin sheath, and as before mentioned, PLP1 mutations have been widely studied as cause of PMD and SPG2. Studying male affected patients and animal models has led us to defy PLP1/DM20 actively participate in the synthesis of myelin intraperiod line, myelin compaction, myelin sheath adhesion to oligodendrocyte membrane, etc.
To use SPG2 algorithm to solve CNOP directly, we let [J.sub.o]([x.sub.0[delta]]) = -J([x.sub.0[delta]]); then the optimization problem in (3) is equivalent to the following optimization problem:
Now, the optimization problem has been converted into solving minimum value of the objective function, which is the same form with problem in (4); therefore, we can use SPG2 method directly.
The primary idea of our approach is to calculate the gradient of objective function with respect to the initial perturbation [x.sub.0] using the gradient definition in mathematics firstly and then to apply spg2 method to solve CNOP based on the gradient information.
Then we use SPG2 algorithm to calculate CNOP, the maximum iteration steps are set as 20 for stopping criterion, the gradient(), values(), and line_search() represent related subroutines, the gradient() subroutine calculates the gradient by implementing formulas (8) and (9), the values() subroutine calculates the value of objective function in current position, and line_search() subroutine searches the next position along the direction of gradient decent.
Initialization: (1) Set the parameters [nabla], [delta], n, [x.sub.0] SPG2: (2) Calculate the gradient of [x.sub.0] with respect to the objective function using subroutine gradient([x.sub.0]) (3) Calculate the value of objective function in [x.sub.0] using subroutine values([x.sub.0]) (4) While (the stopping criterion is not satisfied) do (5) Calculate the new position x' using subroutine line_search() (6) Calculate the gradient in x' using subroutine gradient(x') (7) End while Output: CNOP (the x' when the value of values(x') is the minimum for all x') 3.2.