Trust region (TR) algorithms are a class of recently developed algorithms for nonlinearoptimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is pre...
详细信息
Trust region (TR) algorithms are a class of recently developed algorithms for nonlinearoptimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously, differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.
Controlling the nonlinear process is a very challenging task in the process plant, whereby it depends on the practitioners' knowledge and skills. This paper aims at devel-oping Gain Scheduling (GS) based controlle...
详细信息
Controlling the nonlinear process is a very challenging task in the process plant, whereby it depends on the practitioners' knowledge and skills. This paper aims at devel-oping Gain Scheduling (GS) based controller tunings to obtain the trade-off controller tun-ings for both servo and regulatory control objectives at the Low, Medium and high oper-ating levels supported by optimization analysis. At first, the research obtains First Order plus Dead Time (FOPDT) models of various operating levels from the Gravity Drained function of LOOP-PRO software. The dynamic characteristics of GA are compared with Particle Swarm optimization (PSO), which showed GA produced more desirable re-sponses and performance indexes. The analysis also compares process responses and per-formance indexes of GA with manually calculated controller tunings. The overall result shows that GA optimization analysis produces the most reasonable controller tunings for consistent control performance compared to other methods. Ultimately, GA algorithms were adopted into a Graphical User Interface (GUI) of MATLAB software, allowing the automated generation of the controller tunings for the identified models.
In this paper, we propose a nonmonotone trust-region algorithm for the solution of optimization problems with general nonlinear equality constraints and simple bounds. Under a constant rank assumption on the gradients...
详细信息
In this paper, we propose a nonmonotone trust-region algorithm for the solution of optimization problems with general nonlinear equality constraints and simple bounds. Under a constant rank assumption on the gradients of the active constraints, we analyze the global convergence of the proposed algorithm.
This paper is concerned with the numerical solution of a Karush-Kuhn-Tucker system. Such symmetric indefinite system arises when we solve a nonlinear programming problem by an Interior-Point (IP) approach. In this fra...
详细信息
This paper is concerned with the numerical solution of a Karush-Kuhn-Tucker system. Such symmetric indefinite system arises when we solve a nonlinear programming problem by an Interior-Point (IP) approach. In this framework, we discuss the effectiveness of two inner iterative solvers: the method of multipliers and the preconditioned conjugate gradient method. We discuss the implementation details of these algorithms in an IP scheme and we report the results of a numerical comparison on a set of large scale test-problems arising from the discretization of elliptic control problems.
In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier i...
详细信息
In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing specific performance measure directly, we circumvent this problem by proposing a novel two-step approach called CAPO, namely, to first train nonlinear auxiliary classifiers with existing learning methods and then to adapt auxiliary classifiers for specific performance measures. In the first step, auxiliary classifiers can be obtained efficiently by taking off-the-shelf learning algorithms. For the second step, we show that the classifier adaptation problem can be reduced to a quadratic program problem, which is similar to linear SVMperf and can be efficiently solved. By exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear classifier which optimizes a large variety of performance measures, including all the performance measures based on the contingency table and AUC, while keeping high computational efficiency. Empirical studies show that CAPO is effective and of high computational efficiency, and it is even more efficient than linear SVMperf.
Conventional approaches for fixed-point implementation of digital signal processing algorithms require the scaling and word-length (WL) optimization in the algorithm level and the high-level synthesis for functional u...
详细信息
Conventional approaches for fixed-point implementation of digital signal processing algorithms require the scaling and word-length (WL) optimization in the algorithm level and the high-level synthesis for functional unit sharing in the architecture level. However, the algorithm-level WL optimization has a few limitations because it can neither utilize the functional unit sharing information for signal grouping nor estimate the hardware cost for each operation accurately. In this study, we develop a combined WL optimization and high-level synthesis algorithm not only to minimize the hardware implementation cost, but also to reduce the optimization time significantly, This software initially finds the WL sensitivity or minimum WL of each signal throughout fixed-point simulations of a signal flow graph, performs the WL conscious high-level synthesis where signals having the similar WL sensitivity are assigned to the same functional unit, and then conducts the final WL optimization by iteratively modifying the WLs of the synthesized hardware model. A list-scheduling-based and an integer linear-programming-based algorithms are developed for the WL conscious high-level synthesis. The hardware cost function to minimize is generated by using a synthesized hardware model. Since fixed-point simulation is used to measure the performance, this method can be applied to general, including nonlinear and time-varying, digital signal processing systems. A fourth-order infinite-impulse response filter, a fifth-order elliptic filter, and a 12th-order adaptive least mean square filter are implemented using this software.
System identification is an important means for obtaining dynamical models for process control applications;experimental testing represents the most time-consuming step in this task. The design of constrained, '&#...
详细信息
System identification is an important means for obtaining dynamical models for process control applications;experimental testing represents the most time-consuming step in this task. The design of constrained, '' plant-friendly '' multisine input signals that optimize a geometric discrepancy criterion arising from Weyl's Theorem is examined in this paper. Such signals are meaningful for data-centric estimation methods, where uniform coverage of the output state-space is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear problem example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy. The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has great benefits compared to multisine signals that minimize crest factor. The constrained nonlinearoptimization problems that are solved represent challenges even for high-performanceoptimizationsoftware.
A rigorous convergence analysis for the fixed point ICA algorithm of Hyvarinen and Oja is provided and a generalization of it involving cumulants of an arbitrary order is presented. We consider a specific optimization...
详细信息
A rigorous convergence analysis for the fixed point ICA algorithm of Hyvarinen and Oja is provided and a generalization of it involving cumulants of an arbitrary order is presented. We consider a specific optimization problem OP(p), p > 3, integer, arising from a Blind Source Extraction problem (BSE) and prove that every local maximum of OP(p) is a solution of (BSE) in sense that it extracts one source signal from a linear mixture of unknown statistically independent signals. An algorithm for solving OP(p) is constructed, which has a rate of convergence p - 1.
With the prevalence of big data, MapReduce has emerged as the most widely deployed computing framework for data analysts. This paper addresses MapReduce job performanceoptimization, targeting system latency reduction...
详细信息
ISBN:
(纸本)9781479989379
With the prevalence of big data, MapReduce has emerged as the most widely deployed computing framework for data analysts. This paper addresses MapReduce job performanceoptimization, targeting system latency reduction. We design a systematic method to optimize MapReduce job execution process by maximizing the utilization of computing resources. Through careful analysis of the mechanism behind Hadoop, the map-shuffle-reduce work-flow is formalized based on the resource supply-demand relations. Efficient and effective algorithms are developed to address the optimization using mixed-integer nonlinear programming. Experiments on a ten-node cluster demonstrate that the proposed model achieves consistently improved performance, and significantly outperforms the system with default parameter setting.
Constrained optimization problems occur in many applications of engineering, science and medicine. Much attention has recently been devoted to solving this class of problems using trust region algorithms with strong c...
详细信息
Constrained optimization problems occur in many applications of engineering, science and medicine. Much attention has recently been devoted to solving this class of problems using trust region algorithms with strong convergence properties, in part because of the availability of reliable software. This paper presents a survey of recent advances in trust region algorithms. We then explain the different choices of penalty function, Lagrange function and expanded Lagrangian function used for modeling constrained optimization problems and solving these equations using trust region algorithms. Finally, some numerical results for the implementation of our proposed method on different test problems with various sizes are presented. (C) 1999 Elsevier Science B.V. and IMACS. All rights reserved.
暂无评论