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The method operates on a sequence of solution approximations of different polynomial orders.
Thus, it is important to study Painleve-Kuratowski upper and lower convergences of the sequence of solution sets.
Experimental results suggest, however, that the sequence of solution policies associated with each iteration of the algorithm converges much more rapidly than does the value function.
In Theorem 3.1 and Remark 3.2, we not only give the condition of the existence of a unique positive solution, but also establish an iterative sequence of solution and error estimation.
Moreover, (i) ( λ, u ) ∈ C 1 + with ∥u∥∞ = ξ2j- 1 for some j ∈ ℕ* implies that λ ≥ 2; (ii) ( λ, u ) ∈ C 1 + with ∥u∥∞ = ξ2j for some j ∈ ℕ* implies that λ ≤ 1 2. Actually, such continua C 1 + can be obtained as upper limits in the sense of Kura-towski of sequence of solution continua from associated continuous problems.
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Then a sequence of solutions converging to zero is obtained.
Consider the sequence of solutions of problems (5.21),,.
Any algorithm can generate only an approximating sequence of solutions.
So we can get an unbounded sequence of solutions of (1.1), and the solutions are radial.
This method gives an approximate sequence of solutions converging to a global solution of the problem.
The generalized solution of problem (1.2 - 1.3) can be approximated by a sequence of solutions of problems with initial condition (2.1 - 2.3 2.1 - 2.3
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