We consider a basic resource allocation game, where the players' strategy spaces are subsets of @math and cost utility functions are parameterized by some common vector @math and, otherwise, only depend on the own strategy choice. A strategy of a player can be interpreted as a vector of resource consumption and a joint strategy profile naturally leads to an aggregate consumption vector. We assume that resources can be priced, that is, the game is augmented by a price vector @math and players have quasi-linear overall costs utilities meaning that in addition to the original costs utilities, a player needs to pay the corresponding price per consumed unit. We investigate the following question: for which aggregated consumption vectors @math can we find prices @math that induce an equilibrium realizing the targeted consumption profile? For answering this question, we develop a duality-based framework and derive a characterization of the existence of such @math and @math . We show that our characterization can help to unify parts of three largely independent streams in the literature -- tolls in transportation systems, Walrasian market equilibria and congestion control in communication networks. Besides reproving existing results we establish novel existence results by drawing connections to polyhedral combinatorics and discrete convexity.
Our first main result (Theorem ) relies on a decomposition property of the Lagrangian (for separable problems) and the use of Lagrange multipliers for pricing the resources. This approach is not new and has been developed before, see for instance Bertsekas and Ghallager @cite_16 and Palomar and Chiang @cite_44 . In particular, motivated by the dual-decomposition of the convex programming formulation of the bandwidth allocation problem of @cite_56 , Palomar and Chiang @cite_44 described how the Lagrangian of a general separable optimization problem [ i N U_i( x_i) x_i X_i, i N, ; i N h_i( x_i) u ] can be decomposed into @math independent problems. The difference of this model to ours is the parameterization of the cost utility functions @math with respect to the capacity vector @math . This degree of freedom is a strict generalization and allows to model dependencies of targeted capacity vectors with respect to the intrinsic cost utilities - a prime example appears in nonatomic congestion games, where the cost function of an agent depends on the aggregated load vector. Moreover, this dependency allows to model with respect to allocations which are not possible in the model of Palomar and Chiang @cite_44 .
@cite_44 @cite_16 @cite_56
2164571150 2034600319 2035233604 2082565560
One central issue in practically deploying network coding is the adaptive and economic allocation of network resource. We cast this as an optimization, where the net-utility-the difference between a utility derived from the attainable multicast throughput and the total cost of resource provisioning-is maximized. By employing the MAX of flows characterization of the admissible rate region for multicasting, this paper gives a novel reformulation of the optimization problem, which has a separable structure. The Lagrangian relaxation method is applied to decompose the problem into subproblems involving one destination each. Our specific formulation of the primal problem results in two key properties. First, the resulting subproblem after decomposition amounts to the problem of finding a shortest path from the source to each destination. Second, assuming the net-utility function is strictly concave, our proposed method enables a near-optimal primal variable to be uniquely recovered from a near-optimal dual variable. A numerical robustness analysis of the primal recovery method is also conducted. For ill-conditioned problems that arise, for instance, when the cost functions are linear, we propose to use the proximal method, which solves a sequence of well-conditioned problems obtained from the original problem by adding quadratic regularization terms. Furthermore, the simulation results confirm the numerical robustness of the proposed algorithms. Finally, the proximal method and the dual subgradient method can be naturally extended to provide an effective solution for applications with multiple multicast sessions In citepapa, Papadimitriou formalized the notion of routing stability in BGP as the following coalitional game theoretic problem: Given a network with a multicommodity flow satisfying node capacity and demand constraints, the payoff of a node is the total flow originated or terminated at it. A payoff allocation is in the core if and only if there is no subset of nodes that can increase their payoff by seceding from the network. We answer one of the open problems in citepapa by proving that for any network, the core is non-empty in both the transferable (where the nodes can compensate each other with side payments) and the non-transferable case. In the transferable case we show that such an allocation can be computed in polynomial time. We also generalize this result to the case where a strictly concave utility function is associated with each commodity. In this paper, we study decomposition methods based on separable approximations for minimizing the augmented Lagrangian. In particular, we study and compare the diagonal quadratic approximation method DQAM of Mulvey and Ruszczynski [A diagonal quadratic approximation method for large scale linear programs, Oper. Res. Lett. 12 1992, pp. 205–215] and the parallel coordinate descent method PCDM of Richtarik and Takac [Parallel coordinate descent methods for big data optimization. Technical report, November 2012. arXiv:1212.0873]. We show that the two methods are equivalent for feasibility problems up to the selection of a step-size parameter. Furthermore, we prove an improved complexity bound for PCDM under strong convexity, and show that this bound is at least 8L′ Lω−12 times better than the best known bound for DQAM, where ω is the degree of partial separability and L’ and L are the maximum and average of the block Lipschitz constants of the gradient of the quadratic penalty appearing in the augmented Lagrangian. In this paper, we study the resource allocation algorithm design for multiuser orthogonal frequency division multiplexing (OFDM) downlink systems with simultaneous wireless information and power transfer. The algorithm design is formulated as a non-convex optimization problem for maximizing the energy efficiency of data transmission (bit Joule delivered to the users). In particular, the problem formulation takes into account the minimum required system data rate, heterogeneous minimum required power transfers to the users, and the circuit power consumption. Subsequently, by exploiting the method of timesharing and the properties of nonlinear fractional programming, the considered non-convex optimization problem is solved using an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation and user selection solution are derived based on Lagrange dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm achieves the maximum energy efficiency of the system and reveal how energy efficiency, system capacity, and wireless power transfer benefit from the presence of multiple users in the system.