1,382 research outputs found
Primal and dual multi-objective linear programming algorithms for linear multiplicative programmes
Multiplicative programming problems (MPPs) are global optimization problems known to be NP-hard. In this paper, we employ algorithms developed to compute the entire set of nondominated points of multi-objective linear programmes (MOLPs) to solve linear MPPs. First, we improve our own objective space cut and bound algorithm for convex MPPs in the special case of linear MPPs by only solving one linear programme in each iteration, instead of two as the previous version indicates. We call this algorithm, which is based on Benson’s outer approximation algorithm for MOLPs, the primal objective space algorithm. Then, based on the dual variant of Benson’s algorithm, we propose a dual objective space algorithm for solving linear MPPs. The dual algorithm also requires solving only one linear programme in each iteration. We prove the correctness of the dual algorithm and use computational experiments comparing our algorithms to a recent global optimization algorithm for linear MPPs from the literature as well as two general global optimization solvers to demonstrate the superiority of the new algorithms in terms of computation time. Thus, we demonstrate that the use of multi-objective optimization techniques can be beneficial to solve difficult single objective global optimization problems
The Fidelity of Recovery is Multiplicative
Fawzi and Renner [Commun. Math. Phys. 340(2):575, 2015] recently established
a lower bound on the conditional quantum mutual information (CQMI) of
tripartite quantum states in terms of the fidelity of recovery (FoR),
i.e. the maximal fidelity of the state with a state reconstructed from
its marginal by acting only on the system. The FoR measures quantum
correlations by the local recoverability of global states and has many
properties similar to the CQMI. Here we generalize the FoR and show that the
resulting measure is multiplicative by utilizing semi-definite programming
duality. This allows us to simplify an operational proof by Brandao et al.
[Phys. Rev. Lett. 115(5):050501, 2015] of the above-mentioned lower bound that
is based on quantum state redistribution. In particular, in contrast to the
previous approaches, our proof does not rely on de Finetti reductions.Comment: v2: 9 pages, published versio
A Fast Distributed Stateless Algorithm for -Fair Packing Problems
Over the past two decades, fair resource allocation problems have received
considerable attention in a variety of application areas. However, little
progress has been made in the design of distributed algorithms with convergence
guarantees for general and commonly used -fair allocations. In this
paper, we study weighted -fair packing problems, that is, the problems
of maximizing the objective functions (i) when , and (ii) when , over linear constraints , ,
where are positive weights and and are non-negative. We consider
the distributed computation model that was used for packing linear programs and
network utility maximization problems. Under this model, we provide a
distributed algorithm for general that converges to an
approximate solution in time (number of distributed iterations)
that has an inverse polynomial dependence on the approximation parameter
and poly-logarithmic dependence on the problem size. This is the
first distributed algorithm for weighted fair packing with
poly-logarithmic convergence in the input size. The algorithm uses simple local
update rules and is stateless (namely, it allows asynchronous updates, is
self-stabilizing, and allows incremental and local adjustments). We also obtain
a number of structural results that characterize fair allocations as
the value of is varied. These results deepen our understanding of
fairness guarantees in fair packing allocations, and also provide
insight into the behavior of fair allocations in the asymptotic cases
, , and .Comment: Added structural results for asymptotic cases of \alpha-fairness
(\alpha approaching 0, 1, or infinity), improved presentation, and revised
throughou
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