On the λ-Robustness of Fuzzy Neutrosophic Soft Matrix

Let (N, ≤) be a non-empty, bounded, linearly ordered set a a ⊕ b = max{a, b}, a ⊗ b = min{a, b} for ab∈ N.A fuzzy neutrosophic soft vector (FNSV) 〈 x^T, x^I, x^F 〉 is said to be a λ-fuzzy neutrosophic soft eigenvector (FNSEv) of a square fuzzy neutrosophic soft matrix (FNSM) A if A ⊗ x =λ⊗ x for some λ∈ N. A given FNSM A is called (strongly) λ-robust if for every x the FNSV A^k ⊗ x is a (greatest) FNSEv of A for some natural number k. We present a characterization of λ-robust and strongly λ-robust FNSMs. Building on this, an efficient algorithm for checking the λ robustness and strong λ-robustness of a given FNSM is introduced.