Fastpass
... • A core arbiter would have to handle a large volume of traffic, so allocating at MTU-size granularity would not be computationally feasible. Solution：the use of specialized hardware ...
... • A core arbiter would have to handle a large volume of traffic, so allocating at MTU-size granularity would not be computationally feasible. Solution：the use of specialized hardware ...
To Transmit or Not to Transmit? Distributed Queueing Games in
... (e.g., transmit power, rate allocation, or precoding matrices) over queueing networks based only on local state information remains a difficult and open problem. Good examples of efforts in this direction are [16], [23]–[31] (see also references therein). In these works, the authors designed distribu ...
... (e.g., transmit power, rate allocation, or precoding matrices) over queueing networks based only on local state information remains a difficult and open problem. Good examples of efforts in this direction are [16], [23]–[31] (see also references therein). In these works, the authors designed distribu ...
A Bucket Elimination Approach for Determining Strong
... This work’s motivation stems from the study of robust task execution in uncertain environments, grounded in the application of robotic manufacturing. Among its many challenges, it is important to highlight the key role played by uncontrollable choices. Besides modeling runtime “exceptions”, uncontro ...
... This work’s motivation stems from the study of robust task execution in uncertain environments, grounded in the application of robotic manufacturing. Among its many challenges, it is important to highlight the key role played by uncontrollable choices. Besides modeling runtime “exceptions”, uncontro ...
Slide 1
... activities, the route to be followed by a vehicle through a traffic network, or the policies to be advocated by a candidate. ...
... activities, the route to be followed by a vehicle through a traffic network, or the policies to be advocated by a candidate. ...
Set point control in the state space setting
... In this section we will discuss control formulated in terms control moves. This can be regarded as if the decision variable is the velocity of the control. It can be extended to include the derivative of the control action to any order. If an optimal control strategy from section 5 or 6 is applied o ...
... In this section we will discuss control formulated in terms control moves. This can be regarded as if the decision variable is the velocity of the control. It can be extended to include the derivative of the control action to any order. If an optimal control strategy from section 5 or 6 is applied o ...
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[slides] Sensor network applications
... [Karlof+ 2002] C. Karlof, Y. Li and J. Polastre, ARRIVE: Algorithm for Robust Routing in Volatile ...
... [Karlof+ 2002] C. Karlof, Y. Li and J. Polastre, ARRIVE: Algorithm for Robust Routing in Volatile ...
Nonnegative Matrix Factorization with Sparseness Constraints
... First need some way to define the sparseness of a vector A vector with one nonzero entry is maximally sparse A multiple of the vector of all ones, e, is minimally sparse CBS Inequality How can we combine these ideas? ...
... First need some way to define the sparseness of a vector A vector with one nonzero entry is maximally sparse A multiple of the vector of all ones, e, is minimally sparse CBS Inequality How can we combine these ideas? ...
Structure discovery in PPI networks using pattern
... • An algorithm for one such problem, sub-graph isomorphism, that is more efficient than previous algorithms. • In concert with suitable query patterns that exploit some simple properties of graphs, query-based graph search can be used to examine network structure at a scale that reveals relationship ...
... • An algorithm for one such problem, sub-graph isomorphism, that is more efficient than previous algorithms. • In concert with suitable query patterns that exploit some simple properties of graphs, query-based graph search can be used to examine network structure at a scale that reveals relationship ...
Managing Dynamic Temporal Constraint Networks
... quantitative temporal constraints plays a significant role in the problem solving architectures that cope with realistic situations. Dealing with quantitative time has always been an important issue both in planning [VER83, BEL86, DEA87] and in scheduling [RIT86, LEP87]. Dean's TMM [DEA87, 89] repre ...
... quantitative temporal constraints plays a significant role in the problem solving architectures that cope with realistic situations. Dealing with quantitative time has always been an important issue both in planning [VER83, BEL86, DEA87] and in scheduling [RIT86, LEP87]. Dean's TMM [DEA87, 89] repre ...
Document
... Interpretation: if wr is file size, then wr/xr is transfer time; optimization problem is to minimize sum of transfer delays ...
... Interpretation: if wr is file size, then wr/xr is transfer time; optimization problem is to minimize sum of transfer delays ...
Parameter Estimation with Expected and Residual-at
... uncertainty affecting the data is characterized by means of its first two statistical moments, and the above criteria are intended in a worst-case probabilistic sense, that is worst-case expectations and probabilities over all possible distribution having the specified moments are considered. The en ...
... uncertainty affecting the data is characterized by means of its first two statistical moments, and the above criteria are intended in a worst-case probabilistic sense, that is worst-case expectations and probabilities over all possible distribution having the specified moments are considered. The en ...
Achieving Maximum Energy-Efficiency in Multi-Relay
... power constraints. In the class of power minimization problems, an example is the often-cited work by Wong et. al [12], where a heuristic bit allocation algorithm was conceived for a multi-user OFDMA system with the aim of minimizing the power consumption under a minimum individual user rate constra ...
... power constraints. In the class of power minimization problems, an example is the often-cited work by Wong et. al [12], where a heuristic bit allocation algorithm was conceived for a multi-user OFDMA system with the aim of minimizing the power consumption under a minimum individual user rate constra ...
Which component and method is slowing down the
... This report reveals which components are spending the most time running on the machine. The total duration of all component instances of each component type is calculated, and then the component types are sorted by total duration. This view helps you to choose which components are likely performance ...
... This report reveals which components are spending the most time running on the machine. The total duration of all component instances of each component type is calculated, and then the component types are sorted by total duration. This view helps you to choose which components are likely performance ...
Solutions for the exercises - Delft Center for Systems and Control
... Figure 3: Feasible set and contour plot for Exercise 2.1 Solution: Figure 3 shows the contour plot and the feasible region of the optimization problem. The solution is in a vertex of the feasible set, which is obtained with the graphical method (we shift one of the contour lines in a parallel way in ...
... Figure 3: Feasible set and contour plot for Exercise 2.1 Solution: Figure 3 shows the contour plot and the feasible region of the optimization problem. The solution is in a vertex of the feasible set, which is obtained with the graphical method (we shift one of the contour lines in a parallel way in ...
Optimal design of Kelly / Whittle network
... • (ii) If the routing jk is used in the optimal design the equality holds in (i) and the minimum in the rhs is attained at given k. • (iii) If node j is not used in the optimal design then αj =0. If it is used but at less that full capacity then cj =0. ...
... • (ii) If the routing jk is used in the optimal design the equality holds in (i) and the minimum in the rhs is attained at given k. • (iii) If node j is not used in the optimal design then αj =0. If it is used but at less that full capacity then cj =0. ...
Utility-Maximizing Data Dissemination in Socially Selfish
... knowledge up to T slots into the future. Analysis is missing on how close the throughput utility approaches optimality. For cognitive radio networks, Ding et al. [3], [4] have designed back-pressure protocols for routing with collaborative spectrum sensing, but without utility-optimality guarantee. ...
... knowledge up to T slots into the future. Analysis is missing on how close the throughput utility approaches optimality. For cognitive radio networks, Ding et al. [3], [4] have designed back-pressure protocols for routing with collaborative spectrum sensing, but without utility-optimality guarantee. ...
the simultaneous optimization of building fabric
... cost objective function). Further, some problems have variables that are not explicitly included in the objective function and only serve to satisfy one or more of the constraint functions. For instance, the maximum water flow rate to a coil would not be a direct parameter of the coils capital cost ...
... cost objective function). Further, some problems have variables that are not explicitly included in the objective function and only serve to satisfy one or more of the constraint functions. For instance, the maximum water flow rate to a coil would not be a direct parameter of the coils capital cost ...
Scalability_1.1
... → If W < Θ(p) i.e problem size grows slower than p, as p↑ ↑ => at one point #PE > W =>E↓ ↓ => asymptotically W= Θ(p) Problem size must increase proportional as Θ(p) to maintain fixed efficiency W = Ω(p) (W should grow at least as fast as p) Ω(p) is the asymptotic lower bound on the isoefficiency fun ...
... → If W < Θ(p) i.e problem size grows slower than p, as p↑ ↑ => at one point #PE > W =>E↓ ↓ => asymptotically W= Θ(p) Problem size must increase proportional as Θ(p) to maintain fixed efficiency W = Ω(p) (W should grow at least as fast as p) Ω(p) is the asymptotic lower bound on the isoefficiency fun ...
Dynamic RWA for All Optical Networks Using
... Simulation setup In our scenario we consider the following routing algorithms: without constraints - shortest transparent path (STP) and SATP; with constraints - SATP with wavelength dependent performance (SAPT-WD), SAPT with all wavelengths assumed to perform like the wavelength with the highest Q- ...
... Simulation setup In our scenario we consider the following routing algorithms: without constraints - shortest transparent path (STP) and SATP; with constraints - SATP with wavelength dependent performance (SAPT-WD), SAPT with all wavelengths assumed to perform like the wavelength with the highest Q- ...
Q A format
... Answer: There are two intellectually fresh cornerstones behind “layering as optimization decomposition”. The first is “network as an optimizer”. The idea of “protocol as a distributed solution” to some global optimization problem (in the form of the basic NUM) has been successfully tested in trials ...
... Answer: There are two intellectually fresh cornerstones behind “layering as optimization decomposition”. The first is “network as an optimizer”. The idea of “protocol as a distributed solution” to some global optimization problem (in the form of the basic NUM) has been successfully tested in trials ...
slides
... Other solutions To construct an iMZ , we have to check four constraints : i. Muu = (1 − Zu ) ii. 0 ≤ M u iii. M u ≤ 1 − Z iv. M u ≤ M v for u < v These constraints are easy to handle if M u are solutions of a SDE: The constraint i indicates the initial condition; the constraint ii means that we must ...
... Other solutions To construct an iMZ , we have to check four constraints : i. Muu = (1 − Zu ) ii. 0 ≤ M u iii. M u ≤ 1 − Z iv. M u ≤ M v for u < v These constraints are easy to handle if M u are solutions of a SDE: The constraint i indicates the initial condition; the constraint ii means that we must ...
Distributed Nash Equilibrium Seeking via the Alternating Direction
... In this work, we aim to exploit the benefits of ADMM in the context of finding an NE of a game. Here are the difficulties that we need to overcome: • A Nash game can be seen as a set of parallel optimization problems, each of them associated with the minimization of a player’s own cost function w.r. ...
... In this work, we aim to exploit the benefits of ADMM in the context of finding an NE of a game. Here are the difficulties that we need to overcome: • A Nash game can be seen as a set of parallel optimization problems, each of them associated with the minimization of a player’s own cost function w.r. ...