Recently, in [12] a very general class of truncated Newton methods has been proposed for solving large scale unconstrained optimization problems. In this work we present the results of an extensive numerical experienc...
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Recently, in [12] a very general class of truncated Newton methods has been proposed for solving large scale unconstrained optimization problems. In this work we present the results of an extensive numerical experience obtained by different algorithms which belong to the preceding class. This numerical study, besides investigating which are the best algorithmic choices of the proposed approach, clarifies some significant points which underlies every truncated Newton based algorithm.
We present a computationally efficient implementation of an interior point algorithm for solving large-scale problems arising in stochastic linear programming and robust optimization. A matrix factorization procedure ...
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We present a computationally efficient implementation of an interior point algorithm for solving large-scale problems arising in stochastic linear programming and robust optimization. A matrix factorization procedure is employed that exploits the structure of the constraint matrix, and it is implemented on parallel computers. The implementation is perfectly scalable. Extensive computational results are reported for a library of standard test problems from stochastic linear programming, and also for robust optimization formulations. The results show that the codes are efficient and stable for problems with thousands of scenarios. Test problems with 130 thousand scenarios, and a deterministic equivalent linear programming formulation with 2.6 million constraints and 18.2 million variables, are solved successfully.
This paper considers the number of inner iterations required per outer iteration for the algorithm proposed by Conn er al. [9]. We show that asymptotically, under suitable reasonable assumptions, a single inner iterat...
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This paper considers the number of inner iterations required per outer iteration for the algorithm proposed by Conn er al. [9]. We show that asymptotically, under suitable reasonable assumptions, a single inner iteration suffices.
ELSO is an environment for the solution of large-scale optimization problems. With ELSO the user is required to provide only code for the evaluation of a partially separable function. ELSO exploits the partial separab...
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ELSO is an environment for the solution of large-scale optimization problems. With ELSO the user is required to provide only code for the evaluation of a partially separable function. ELSO exploits the partial separability structure of the function to compute the gradient efficiently using automatic differentiation. We demonstrate ELSO's efficiency by comparing the various options available in ELSO. Our conclusion is that the hybrid option in ELSO provides performance comparable to the hand-coded option, while having the significant advantage of not requiring a hand-coded gradient or the sparsity pattern of the partially separable function. In our test problems, which have carefully coded gradients, the computing time for the hybrid AD option is within a factor of two of the hand-coded option.
Sequential quadratic (SQP) programming methods are the method of choice when solving small or medium-sized problems. Since they are complex methods they are difficult (but not impossible) to adapt to solve large-scale...
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Sequential quadratic (SQP) programming methods are the method of choice when solving small or medium-sized problems. Since they are complex methods they are difficult (but not impossible) to adapt to solve large-scale problems. We start by discussing the difficulties that need to be addressed and then describe some general ideas that may be used to resolve these difficulties. A number of SQP codes have been written to solve specific applications and there is a general purposed SQP code called SNOPT, which is intended for general applications of a particular type. These are described briefly together with the ideas on which they are based. Finally we discuss new work on developing SQP methods using explicit second derivatives.
In this paper we present a parallel algorithm for the solution of discrete optimization problems, which is a typical example of highly irregularly structured problems. The Processor Farm model, well suited for this cl...
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ISBN:
(纸本)3540628983
In this paper we present a parallel algorithm for the solution of discrete optimization problems, which is a typical example of highly irregularly structured problems. The Processor Farm model, well suited for this class of problems, has been modified to eliminate the presence of the coordinator, which represents a bottleneck for the parallel computation.
nonlinear constrained optimization problems can be solved by a Lagrange-multiplier method in a continuous space or by its extended discrete version in a discrete space. These methods rely on gradient descents in the o...
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nonlinear constrained optimization problems can be solved by a Lagrange-multiplier method in a continuous space or by its extended discrete version in a discrete space. These methods rely on gradient descents in the objective space to find high-quality solutions, and gradient ascents in the Lagrangian space to satisfy the constraints. The balance between descents and ascents depends on the relative weights between the objective and the constraints that indirectly control the convergence speed and solution quality of the method. To improve convergence speed without degrading solution quality, the authors propose an algorithm to dynamically control these relative weights. Starting from an initial weight, the algorithm automatically adjusts the weights based on the behaviour of the search progress. With this strategy, one is able to eliminate divergence, reduce oscillations, and speed up convergence. They show improved convergence behaviour of the proposed algorithm on both nonlinear continuous and discrete problems.
This paper investigates the application of a recurrent wavelet neural network (RWNN) to the blind equalization of nonlinear communication channels. We propose a RWNN based structure and a novel training approach for b...
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ISBN:
(纸本)0818679190
This paper investigates the application of a recurrent wavelet neural network (RWNN) to the blind equalization of nonlinear communication channels. We propose a RWNN based structure and a novel training approach for blind equalization, and we evaluate its performance via computer simulations for a nonlinear communication channel model. It is shown that the RWNN blind equalizer performs much better than the linear CMA and the RRBF blind equalizers in the nonlinear channel case. The small size and highperformance of the RWNN equalizer makes it suitable for high speed channel blind equalization.
Although the undeniable importance of high quality, efficient and effective DSP synthesis benchmark has been firmly and widely established, until now the emphasis of benchmarking has been restricted on assembling indi...
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Although the undeniable importance of high quality, efficient and effective DSP synthesis benchmark has been firmly and widely established, until now the emphasis of benchmarking has been restricted on assembling individual examples. In this paper we introduce the "ideal candidate benchmark methodology" which poses the development of the benchmark as well as defines a statistical and optimization problem. We first outline the goals and requirements relevant for the benchmark development. After discussing the computational complexity of the benchmark selection problem, we present a simulated annealing-based algorithm for solving this computationally intractable optimization task. Using this approach from 150 examples we select 12 examples for the new DSP Quant benchmark for DSP hard Real-Time applications. The DSP benchmark is statistically validated, and its application to the analysis and development of system-level synthesis algorithms is demonstrated,.
Optimizing performance and power during the design of embedded systems for real-time constrained applications is an important problem. This paper presents a network flow optimization technique to analyze power and per...
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Optimizing performance and power during the design of embedded systems for real-time constrained applications is an important problem. This paper presents a network flow optimization technique to analyze power and performance tradeoffs for memory component design of an embedded system. The optimal number of external and internal memory accesses, memory sizes, and the number of extra computations (or data regeneration) for a number of tasks is determined. This is unlike previous research, which has only discussed ad hoc suggestions for this problem. The network flow approach can be solved to a globally optimal solution in polynomial time using very fast and efficient algorithms. Results for a large, complex, real industrial application-audio compression-show that this network flow technique provides up to 3.11 and 1.44 times improvement in power and performance respectively. This research is important for industry since power, performance and cost considerations at the early stages of design are crucial for mapping high-performance applications into cost-efficient and reliable systems.
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