1974
DOI: 10.1017/s0017089500002135
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Axiomatisations of the average and a further generalisation of monotonic sequences

Abstract: A bounded monotonic sequence is convergent. This paper shows that a bounded sequence which is g-monotonic (to be defined) also converges. The proof generalises one attributed to Professor R. A. Rankin by Copson [1]. The theorem requires two definitions: the first axiomatises the notion of “average“ and the second generalises the concept of monotonicity.

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Cited by 121 publications
(67 citation statements)
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“…Note that the optimization problem (15) has a differentiable objective function and the transmit power constraints only work for variable W . It follows from the general optimization theory [26,27] that steps 3 to 4 in Algorithm 1, which is a block coordinate ascent method applied to problem (15), converge to a stationary point of problem (15). Based on these analyses, the convergence of Algorithm 1 can be guaranteed with the fractional theorem obtained in [18,19].…”
Section: Lemmamentioning
confidence: 99%
See 1 more Smart Citation
“…Note that the optimization problem (15) has a differentiable objective function and the transmit power constraints only work for variable W . It follows from the general optimization theory [26,27] that steps 3 to 4 in Algorithm 1, which is a block coordinate ascent method applied to problem (15), converge to a stationary point of problem (15). Based on these analyses, the convergence of Algorithm 1 can be guaranteed with the fractional theorem obtained in [18,19].…”
Section: Lemmamentioning
confidence: 99%
“…Since the achievable SINR region under the transmit power constraint is bounded, the sum rate is also bounded. The convergence of Algorithm 1 is guaranteed by the monotonic convergence theorem [26,27]. Note that the optimization problem (15) has a differentiable objective function and the transmit power constraints only work for variable W .…”
Section: Lemmamentioning
confidence: 99%
“…Since the achievable rate region under the sum power constraint is bounded, the convergence of Algorithm 1 is guaranteed by the monotonic convergence theorem [56]. Meanwhile, the studies in [46] have revealed that the convergence of the proposed update method based on the sub-gradient ellipsoid method for the auxiliary variables λ is guaranteed to minimize ψ (λ).…”
Section: Algorithm Convergence and Complexity Analysismentioning
confidence: 99%
“…Similar to problem (23), problem (62) can also be solved by using the powerful this, the convergence of Algorithm 5 is also guaranteed by the monotonic boundary theorem [56].…”
Section: An Extended Jbopa Optimization Algorithmmentioning
confidence: 99%
“…Moreover, the value of MSE is bounded in practical wireless communication systems. Therefore, the convergence of Algorithm 1 is guaranteed by the monotonic boundary theorem [36].…”
Section: Theorem 1 Algorithm 1 Guarantees To Convergementioning
confidence: 99%