Researchers consider the observation of a faster, non-maneuvering target by a slower observer in 3-dimensional (3-D) Cartesian space, rather than in the plane. The observation problem is commonly referred to as intell...
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Researchers consider the observation of a faster, non-maneuvering target by a slower observer in 3-dimensional (3-D) Cartesian space, rather than in the plane. The observation problem is commonly referred to as intelligence, surveillance, and reconnaissance (ISR). Observation of a slower ground vehicle by a single or team of faster aerial platforms has been considered. Researchers have also considered the tracking problem of a ground moving target in an urban terrain, with and without airspace limitations. They have considered the task of tracking the ground vehicle by either a single or team of unmanned air vehicles. To handle the computational complexity from numerous mobile agents, the researchers made use of an evolutionary algorithm (EA) to find optimal strategies for the multiple UAVs.
An optimal real-time neural-network-based controller for an on-orbit service mission is proposed. The problem can be mathematically formulated as a complex optimal control problem due to the coupling nature of the orb...
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An optimal real-time neural-network-based controller for an on-orbit service mission is proposed. The problem can be mathematically formulated as a complex optimal control problem due to the coupling nature of the orbit, the attitude, and the manipulator of the chaser spacecraft. A hierarchical optimization procedure is developed to efficiently solve the problem by recursively applying three optimization modules. The relative motion characteristics and the decoupled wrist-arm feature are used to transform the solved optimal solutions into easier learnable samples. A series of deep neural network (DNN) controllers are designed to learn from these samples. The performances of the trained network controllers are analyzed by altering the number of learned trajectories and the structure of networks. Simulation results illustrate that the designed DNN controllers can successfully guide a chaser to approach and capture an uncontrolled target with tolerable errors.
In this paper, we study a class of optimization problems, called Mathematical Programs with Cardinality Constraints (MPCaC). This kind of problem is generally difficult to deal with, because it involves a constraint t...
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In this paper, we study a class of optimization problems, called Mathematical Programs with Cardinality Constraints (MPCaC). This kind of problem is generally difficult to deal with, because it involves a constraint that is not continuous neither convex, but provides sparse solutions. Thereby we reformulate MPCaC in a suitable way, by modeling it as mixed-integer problem and then addressing its continuous counterpart, which will be referred to as relaxed problem. We investigate the relaxed problem by analyzing the classical constraints in two cases: linear and nonlinear. In the linear case, we propose a general approach and present a discussion of the Guignard and Abadie constraint qualifications, proving in this case that every minimizer of the relaxed problem satisfies the Karush-Kuhn-Tucker (KKT) conditions. On the other hand, in the nonlinear case, we show that some standard constraint qualifications may be violated. Therefore, we cannot assert about KKT points. Motivated to find a minimizer for the MPCaC problem, we define new and weaker stationarity conditions, by proposing a unified approach.
Placement is a critical task with high computation complexity in VLSI physical design. Modern analytical placers formulate the placement objective as a nonlinear optimization task, which suffers a long iteration time....
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Sahin Metal was founded in 1975 on a 7500 m(2) area *** to supply high-pressure aluminum casting parts to a variety of industries, especially automotive manufacturers. 64 different products are produced with various r...
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ISBN:
(纸本)9783030904210;9783030904203
Sahin Metal was founded in 1975 on a 7500 m(2) area *** to supply high-pressure aluminum casting parts to a variety of industries, especially automotive manufacturers. 64 different products are produced with various routings through 14 workstations in the plant. Being a Tier 2 company, these products are then sent to Tier 1 firms and finally reach the leading vehicle manufacturers. Nowadays, increasing competition, changing customer demands, and quality targets bring the necessity of restructuring internal processes. In this regard, Sahin Metal plans to rearrange the existing machine layout to minimize the distance traveled between departments by taking into account the material flow. This study aims to determine an efficient machine layout design by implementing analytical approaches. The study is started by visiting the production facility and meeting with the company's engineers to determine the project roadmap. Following that, the data collection process is initiated, and ABC analysis is performed to define product classes. After this identification, two approaches are utilized simultaneously. The Hollier method is employed to find a logical machine arrangement. In addition, a mathematical model based on the Quadratic Assignment Problem (QAP) is developed to obtain the optimum machine layout. The developed integer nonlinear model is solved by CONOPT using GAMS software under various scenarios. Finally, these results are compared with the existing system, and a convenient layout design is proposed to the company.
This paper focuses on the control of a fuel cell system, for which an optimization model is proposed to control the oxygen excess ratio in order to tackle the fuel cell starvation phenomenon and increase the net power...
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This paper focuses on the control of a fuel cell system, for which an optimization model is proposed to control the oxygen excess ratio in order to tackle the fuel cell starvation phenomenon and increase the net power production of the system. The optimization problem considers the detailed nonlinear dynamics of the stack and the air supply sub-system as constraints and upper and lower bounds to consider the technical limitations in the system’s operation. The goal is to maintain the oxygen excess ratio at its reference value, using the compressor input voltage as a control variable, under several load variations of the current demand, considered as the external noise to the system. The proposed nonlinear optimization problem has been applied to a 75 kW stack used in the Ford P2000 fuel cell prototype vehicles, and results have been compared to the proportional-integral (PI) control technique. The proposed control approach has shown a significant enhancement in the control performance of the oxygen excess ratio.
We introduce an Alternating Direction Method of Multipliers (ADMM) for finding a solution of the nonsymmetric Eigenvalue Complementarity Problem (EiCP). A simpler version of this method is proposed for the symmetric E...
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We introduce an Alternating Direction Method of Multipliers (ADMM) for finding a solution of the nonsymmetric Eigenvalue Complementarity Problem (EiCP). A simpler version of this method is proposed for the symmetric EiCP, that is, for the computation of a Stationary Point (SP) of a Standard Fractional Quadratic Program. The algorithm is also extended for the computation of an SP of a Standard Quadratic Program (StQP). Convergence analyses of these three versions of ADMM are presented. The main computational effort of ADMM is the solution of a Strictly Convex StQP, which can be efficiently solved by a Block Principal Pivoting algorithm. Furthermore, this algorithm provides a stopping criterion for ADMM that improves very much its efficacy to compute an accurate solution of the EiCP. Numerical results indicate that ADMM is in general very efficient for solving symmetric EiCPs in terms of the number of iterations and computational effort, but is less efficient for the solution of nonsymmetric EiCPs. However, ADMM is able to provide a good initial point for a fast second-order method, such as the so-called Semismooth Newton method. The resulting hybrid ADMM and SN algorithm seems to be quite efficient in practice for the solution of nonsymmetric EiCPs.
Sparse linear regression is a vast field and there are many different algorithms available to build models. We start here by performing a meta-analysis of the literature to provide recommendations to users about when ...
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Sparse linear regression is a vast field and there are many different algorithms available to build models. We start here by performing a meta-analysis of the literature to provide recommendations to users about when to use which regression algorithm. We then consider Best Subset Selection (BSS), which constructs sparse linear regression models by selecting a subset of potential regression variables to include into the regression model to minimize prediction error while also minimizing complexity. The combinatorial nature of the problem means that BSS may be impractical to solve in many instances. We propose new approximate methods that round the QP-relaxation of the MIQP formulation of the BSS problem to construct good approximate solutions for large BSS problems. We provide theoretical analysis of our algorithms for special cases to give insight into why they perform well. Empirically, our rounding algorithms outperform other popular approaches for subset selection. Building on our work on subset selection, we propose the flexible Regularized Subsets framework that performs variable selection and coefficient shrinkage in two-stages: first using any subset selection method to select variables, then using a convex penalty to estimate coefficients. This approach encourages both sparsity and robustness. We show that Regularized Subsets with rounding for variable selection outperforms other linear model-building approaches in a variety of problem settings. In addition, Regularized Subsets is able to build models from a candidate set of thousands of variables in seconds. Finally, we consider the constrained regression problem. Modelers would often like to further improve accuracy by imposing constraints on the regression coefficients to incorporate a priori knowledge into the linear regression algorithm. We propose the flexible Linearly-Constrained Regularized Subsets framework for this task. The Linearly-Constrained Regularized Subsets framework can easily handle dozen
This paper deals with the optimization of the ascent trajectory of a multistage launch vehicle, from liftoff to the payload injection into the target orbit, considering inverse-square gravity acceleration and aerodyna...
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This paper deals with the optimization of the ascent trajectory of a multistage launch vehicle, from liftoff to the payload injection into the target orbit, considering inverse-square gravity acceleration and aerodynamic forces. A combination of lossless and successive convexification techniques is adopted to generate a sequence of convex problems that rapidly converges to the original problem solution. An automatic initialization strategy is proposed to make the solution process completely autonomous. In particular, a novel three-step continuation procedure is developed and proved to be more efficient than simpler strategies. This approach relies on the solution of intermediate problems, which either neglect atmospheric drag or fix the time-lengths of the launch vehicle ascent phases, that are solved in succession, gradually passing from easier instances of the optimization problem to the originally intended problem. State-of-the-art techniques to deal with such a complex problem are adopted to enhance the convergence rate, including safeguarding modifications, such as virtual controls and an adaptive trust region. To assess the validity of the proposed approach in a practical scenario, numerical results are presented for two representative practical applications, using as reference a Falcon 9 launch vehicle.
We consider the problem of minimizing a sum of several convex non-smooth functions and discuss the selective linearization method (SLIN), which iteratively linearizes all but one of the functions and employs simple pr...
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We consider the problem of minimizing a sum of several convex non-smooth functions and discuss the selective linearization method (SLIN), which iteratively linearizes all but one of the functions and employs simple proximal steps. The algorithm is a form of multiple operator splitting in which the order of processing partial functions is not fixed, but rather determined in the course of calculations. SLIN is globally convergent for an arbitrary number of component functions without artificial duplication of variables. We report results from extensive numerical experiments in two statistical learning settings such as large-scale overlapping group Lasso and doubly regularized support vector machine. In each setting, we introduce novel and efficient solutions for solving sub-problems. The numerical results demonstrate the efficacy and accuracy of SLIN. Published by Elsevier B.V.
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