A global optimal solution to (26) can be obtained by executing the following pseudo-code. min ξ , x ,µ, ζ , ω L ( µ, ω ) such that (A) ω$_{i}$ ≥ − z$_{i}$e ζ$_{i}$ for ∀ i (B) ζ$_{i}$ ≥ α ∑ J j =1 [ x$_{j}$ A$_{ij}$ − Q$_{j}$ ( A T ξ ) j − B$_{j}$ b$_{j}$ ( x$_{j}$ − Q$_{j}$ ) ] , ∀ i (C) ζ$_{i}$ ≥ α J j $_{=1}$[ x$_{j}$ A$_{ij}$ − B$_{j}$ b$_{j}$ x$_{j}$ ], ∀ i (D) µ ≥ 0 (E) x ∈ E ( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ) (F) ξ ∈ X ( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ) (27) for ℓ$_{1}$ = 1 : 1 : n$_{1}$ for ℓ$_{2}$ = 1 : 1 : n$_{2}$ ... for ℓ$_{K}$ = 1 : 1 : n$_{K}$ if E ( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ) =  Solve (27) using the interior point method. The optimal value of the objective function → L $^{∗}$( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ) The optimizing value of x → x $^{∗}$( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ) The optimizing value of ξ → ξ $^{∗}$( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ) end end ... for end arg max$_{ℓ}$$_{1}$$_{,ℓ}$$_{2}$$_{,...,ℓ}$$_{K}$$_{,E}$$_{(}$$_{ℓ}$$_{1}$$_{,ℓ}$$_{2}$$_{,...,ℓ}$$_{K}$$_{)}$$_{̸}$$_{=}$  L $^{∗}$( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ) → ℓ ∗ $_{1}$, ℓ ∗ $_{2}$, ..., ℓ ∗ K x $^{∗}$( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ), ξ $^{∗}$( ℓ$_{1}$, ℓ$_{2}$, ..., ℓ$_{K}$ ) → global optimal solution (28) 4.3.3 The Computational Efficiency In modern complexity analysis, the efficiency of an algorithm is assessed based on whether the number of iterations required is bounded above by a polynomial of the problem dimension (Luen- 22