Search results for "Optimization"
showing 10 items of 2824 documents
Non-convex distributed power allocation games in cognitive radio networks
2013
In this thesis, we explore interweave communication systems in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio user by optimizing jointly both the detection operation based on sensing and the power allocation across channels, taking into account the influence of the sensing accuracy and the interference limitation to the primary users. The optimization problem is addressed in single and multiuser cognitive radio networks for both single-input single-output and multi-input multi-output channels. Firstly, we study the resource allocation optimization problem for single-input single-output single user cognitive radio networks, wherein th…
On the Coincidence of the Feedback Nash and Stackelberg Equilibria in Economic Applications of Differential Games
2002
In this paper the scope of the applicability of the Stackelberg equilibrium concept in differential games is investigated. Firstly, conditions for obtaining the coincidence between the Stackelberg and Nash equilibria are defined in terms of the instantaneous pay-off function and the state equation of the game. Secondly, it is showed that for a class of differential games with state-interdependence both equilibria are identical independently of the player being the leader of the game. A survey of different economic models shows that this coincidence is going to occur for a good number of economic applications of differential games. This result appears because of the continuous-time setting i…
Constrained consensus for bargaining in dynamic coalitional TU games
2011
We consider a sequence of transferable utility (TU) games where, at each time, the characteristic function is a random vector with realizations restricted to some set of values. We assume that the players in the game interact only with their neighbors, where the neighbors may vary over time. The main contributions of the paper are the definition of a robust (coalitional) TU game and the development of a distributed bargaining protocol. We prove the convergence with probability 1 of the bargaining protocol to a random allocation that lies in the core of the robust game under some mild conditions on the players' communication graphs.
Non-cooperative power allocation game with imperfect sensing information for cognitive radio
2012
In this paper, we consider a sensing-based spectrum sharing scenario and present an efficient decentralized algorithm to maximize the total throughput of the cognitive radio users by optimizing jointly both the detection operation and the power allocation, taking into account the influence of the sensing accuracy. This optimization problem can be formulated as a distributed non-cooperative power allocation game, which can be solved by using an alternating direction optimization method. The transmit power budget of the cognitive radio users and the constraint related to the rate-loss of the primary user due to the interference are considered in the scheme. Finally, we use variational inequal…
Efficient Parallel Nash Genetic Algorithm for Solving Inverse Problems in Structural Engineering
2015
A parallel implementation of a game-theory based Nash Genetic Algorithm (Nash-GAs) is presented in this paper for solving reconstruction inverse problems in structural engineering. We compare it with the standard panmictic genetic algorithm in a HPC environment with up to eight processors. The procedure performance is evaluated on a fifty-five bar sized test case of discrete real cross-section types structural frame. Numerical results obtained on this application show a significant achieved increase of performance using the parallel Nash-GAs approach compared to the standard GAs or Parallel GAs.
Thompson Sampling for Dynamic Multi-armed Bandits
2011
The importance of multi-armed bandit (MAB) problems is on the rise due to their recent application in a large variety of areas such as online advertising, news article selection, wireless networks, and medicinal trials, to name a few. The most common assumption made when solving such MAB problems is that the unknown reward probability theta k of each bandit arm k is fixed. However, this assumption rarely holds in practice simply because real-life problems often involve underlying processes that are dynamically evolving. In this paper, we model problems where reward probabilities theta k are drifting, and introduce a new method called Dynamic Thompson Sampling (DTS) that facilitates Order St…
Support Vector Machine and Kernel Classification Algorithms
2018
This chapter introduces the basics of support vector machine (SVM) and other kernel classifiers for pattern recognition and detection. It also introduces the main elements and concept underlying the successful binary SVM. The chapter starts by introducing the main elements and concept underlying the successful binary SVM. Next, it introduces more advanced topics in SVM for classification, including large margin filtering (LMF), SSL, active learning, and large‐scale classification using SVMs. The LMF method performs both signal filtering and classification simultaneously by learning the most appropriate filters. SSL with SVMs exploits the information contained in both labeled and unlabeled e…
Consensus for switched networks with unknown but bounded disturbances
2006
We consider stationary consensus protocols for networks of dynamic agents with switching topologies. The measure of the neighbors' state is affected by Unknown But Bounded disturbances. Here the main contribution is the formulation and solution of what we call the $\epsilon$-consensus problem, where the states are required to converge in a tube of ray $\epsilon$ asymptotically or in finite time.
Nonholonomic Interpolation for Kinematic Problems, Entropy and Complexity
2008
Here we present the main lines of a theory we developed in a series of previous papers, about the motion planning problem in robotics. We illustrate the theory with a few academic examples.
Graph-based algorithms for the efficient solution of a class of optimization problems
2018
In this paper, we address a class of specially structured problems that include speed planning, for mobile robots and robotic manipulators, and dynamic programming. We develop two new numerical procedures, that apply to the general case and to the linear subcase. With numerical experiments, we show that the proposed algorithms outperform generic commercial solvers.