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Thus motivated, we first establish, by introducing a "contribution weight" for each user, the equivalence between (2) and the minimum weighted utility maximization problem.
Appealing to EDT, we decomposed the minimum weighted utility maximization problem into subproblems by using penalty multipliers for constraint violations.
Based on the observation that the global optimum lies on the boundary of the feasible region for unicast communications, we focused on the equivalent but more structured problem in the form of maximizing the minimum weighted utility.
Problem (2) is equivalent to the following minimum weighted utility maximization: maximize min l ∈ L U l ( γ l ( p ) ) x l subject to 0 ≤ p l ≤ P l max, ∀ l ∈ L 0 ≤ x l ≤ 1, ∀ l ∈ L ∑ l ∈ L x l = 1 variables { p, x }. (3).
By using extended duality theory, penalty multipliers are introduced for penalizing the constraint violations, and the minimum weighted utility maximization problem is then decomposed into subproblems for individual users to devise a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the total utility by simulated annealing (SA).
Similar(55)
Minimum Weighted Edit-Distance.
Violations of restricted branch independence refute subjectively weighted utility (SWU), including "stripped" prospect theory.
The optimization problem is formulated with the objective of minimum weighted total bending energy.
Motivated by the relationship, we define a weighted wordtype pattern and a minimum weighted aberration criterion to compare orthogonal arrays of the same parameters.
In this step, minimum weighted restriction is applied to determine the most efficient treatment among all.
We design an O(n3) algorithm to find a minimum weighted coloring of a (P5,¯¯¯¯P5 -free graph.
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minimum weighted degree
minimum external utility
minimum weighted sequence
minimum weighted phase
minimum weighted coloring
minimum weighted vertex
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minimum hot utility
minimum weighted total
minimum weighted sum
minimum weighted mean
minimum weighted service
minimum weighted QoE
minimum weighted response
minimum weighted edge
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