A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary al...
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Most distributed optimization methods used for distributed model predictive control (DMPC) are gradient based. Gradient based optimization algorithms are known to have iterations of low complexity. However, the number...
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ISBN:
(纸本)9781479901777
Most distributed optimization methods used for distributed model predictive control (DMPC) are gradient based. Gradient based optimization algorithms are known to have iterations of low complexity. However, the number of iterations needed to achieve satisfactory accuracy might be significant. This is not a desirable characteristic for distributed optimization in distributed model predictive control. Rather, the number of iterations should be kept low to reduce communication requirements, while the complexity within an iteration can be significant. By incorporating Hessian information in a distributed accelerated gradient method in a well-defined manner, we are able to significantly reduce the number of iterations needed to achieve satisfactory accuracy in the solutions, compared to distributed methods that are strictly gradient-based. Further, we provide convergence rate results and iteration complexity bounds for the developed algorithm.
Discrete mechanics and optimal control (DMOC) is a recent development in optimal control of mechanical systems that takes advantage of the variational structure of mechanics when discretizing the optimal control probl...
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ISBN:
(纸本)9781424477456
Discrete mechanics and optimal control (DMOC) is a recent development in optimal control of mechanical systems that takes advantage of the variational structure of mechanics when discretizing the optimal control problem. Typically, the discrete Euler-Lagrange equations are used as constraints on the feasible set of solutions, and then the objective function is minimized using a constrained optimization algorithm, such as sequential quadratic programming (SQP). In contrast, this paper illustrates that by reducing dimensionality by projecting onto the feasible subspace and then performing optimization, one can obtain significant improvements in convergence, going from superlinear to quadratic convergence. Moreover, whereas numerical SQP can run into machine precision problems before terminating, the projection-based technique converges easily. Double and single pendulum examples are used to illustrate the technique.
This is a set of lecture notes for a Ph.D.-level course on quantumalgorithms, with an emphasis on quantum optimization algorithms. It isdeveloped for applied mathematicians and engineers, and requires no previousbackg...
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Recently Raghavendra and Tan (SODA 2012) gave a 0.85 approximation algorithm for the Max Bisection problem. We improve their algorithm to a 0.8776-approximation. As Max Bisection is hard to approximate within α_(GW)...
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ISBN:
(纸本)9781611972511
Recently Raghavendra and Tan (SODA 2012) gave a 0.85 approximation algorithm for the Max Bisection problem. We improve their algorithm to a 0.8776-approximation. As Max Bisection is hard to approximate within α_(GW) + ε ≈ 0.8786 under the Unique Games Conjecture (UGC), our algorithm is nearly optimal. We conjecture that Max Bisection is approximable within α_(GW)-ε, i.e., the bisection constraint (essentially) does not make Max Cut harder. We also obtain an optimal algorithm (assuming the UGC) for the analogous variant of MAX 2-Sat. Our approximation ratio for this problem exactly matches the optimal approximation ratio for MAX 2-Sat, i.e., α_(LLZ)+ε≈ 0.9401, showing that the bisection constraint does not make MAX 2-Sat harder. This improves on a 0:93-approximation for this problem due to Raghavendra and Tan.
Receiver Operating Characteristic (ROC) curves are useful for evaluation in binary classification and changepoint detection, but difficult to use for learning since the Area Under the Curve (AUC) is piecewise constant...
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In this study a Cascaded αβ delayed signal cancellation based frequency locked loop (Cαβ-DSCFLL) is proposed in the control scheme of the distributed static compensator under disturbed ac main currents with Power ...
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ISBN:
(纸本)9781728104201
In this study a Cascaded αβ delayed signal cancellation based frequency locked loop (Cαβ-DSCFLL) is proposed in the control scheme of the distributed static compensator under disturbed ac main currents with Power Factor Correction mode (PFC). In this algorithm reduced order generalized integrator type FLL has been improved by employing delayed signal cancellation technique. This application of delayed signal cancellation in cascaded mode has improved the THD of supply current. The Cαβ-DSC operators increase the ability of the standard FLL in respect to load disturbance. The PI controller gains have been estimated using an algorithm specific, parameter less type optimization algorithm named JAYA optimization algorithm. The error minimization of DC bus voltage has been formulated as an unconstrained optimization problem and gains have been estimated using JAYA algorithm. The proposed control scheme, with optimized controller gains has handled issues like reactive power compensation and grid current harmonics mitigation for an uncontrolled three phase full bridge rectifier type of nonlinear load. This paper provides results of d-SPACE based real time implementation of the proposed Cαβ-DSCFLL control method for DSTATCOM in three wire system with diode based nonlinear load.
Two classes of algorithms for optimization in the presence of noise are presented, that do not require the evaluation of the objective function. The first generalizes the well-known Adagrad method. Its complexity is t...
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This paper addresses the problem of fault detection filter design for networked control systems (NCS) subject to limited communication capacity and a class of sensor stuck faults. Considering the communication limitat...
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ISBN:
(纸本)9781467325813
This paper addresses the problem of fault detection filter design for networked control systems (NCS) subject to limited communication capacity and a class of sensor stuck faults. Considering the communication limitations (e.g., measurement quantization, signal transmission delays, and data packet dropouts) and all possible sensor stuck faults, a unified mathematical model is first presented. Based on this framework, a full-order fault detection filter is designed such that the residual system is asymptotically stable with the prescribed attenuation level in the generalized H_∞ sense. In order to further improve the detection performance, an optimization algorithm is proposed to minimize the threshold. Finally, a spring-mass-damper system is utilized to show the effectiveness of the proposed method.
There are many applications that can benefit from a well planned search. Whether the search objective is a lost hiker, a stolen vehicle on the interstate, or enemies on the battlefield, some assumptions must be made a...
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ISBN:
(纸本)9781424418060
There are many applications that can benefit from a well planned search. Whether the search objective is a lost hiker, a stolen vehicle on the interstate, or enemies on the battlefield, some assumptions must be made about the search objective before the search can begin. These assumptions focus the search in areas that have a relatively high likelihood of finding the targets of interest. A common approach to mission planning is to apply an optimization algorithm and obtain a good solution based on these assumptions. In general, the mission planner uses simulated targets to emulate the expected target behavior in order to evaluate candidate search paths. In practice, a major drawback is that the prior distribution of targets is only used to evaluate the search paths rather than to guide the optimization algorithm in generating the search paths. This paper introduces a method that explicitly exploits the sampled target distribution to create search paths, which naturally improves the results since the search paths directly depend on the time varying target locations. Results from a realistic cooperative path planning scenario show that explicit usage of target distributions can improve the performance of particle swarm optimization.
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