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In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for univariate data. In this contribution, an extension of such an on-the-fly strategy to multivariate data is investigated. The proposed algorithm relies on the local validation of the Karush-Kuhn-Tucker conditions on the dual problem. Showing that the non-local nature of the multivariate setting precludes to obtain an exact on-the-fly solution, we devise an on-the-fly algorithm delivering an approximate solution, whose quality is controlled by a practitioner-tunable parameter, acting as a trade-off between quality and computational cost. Performance assessment shows that high quality solutions are obtained on-the-fly while benefiting of computational costs several orders of magnitude lower than standard iterative procedures. The proposed algorithm thus provides practitioners with an efficient multivariate change-point detection on-the-fly procedure. [ABSTRACT FROM PUBLISHER]

This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constraints. The proposed method does not use a penalty function and it builds surrogates for the objective and constraint functions. Each parent generates a large number of trial offspring in each generation. Then, the surrogate functions are used to identify the trial offspring that are predicted to be feasible with the best predicted objective function values or those with the minimum number of predicted constraint violations. The objective and constraint functions are then evaluated only on the most promising trial offspring from each parent, and the method proceeds in the same way as in a standard EP. In the numerical experiments, the type of surrogate used to model the objective and each of the constraint functions is a cubic radial basis function (RBF) augmented by a linear polynomial. The resulting RBF-assisted EP is applied to 18 benchmark problems and to an automotive problem with 124 decision variables and 68 black-box inequality constraints. The proposed method is much better than a traditional EP, a surrogate-assisted penalty-based EP, stochastic ranking evolution strategy, scatter search, and CMODE, and it is competitive with ConstrLMSRBF on the problems used. [ABSTRACT FROM PUBLISHER]

We formulate and solve the problem of optimal channel coding and flow-rate control for serving deadline-constrained traffic with average delivery ratio requirements (typical of multimedia streaming and interactive real-time applications) over time-varying channels. To that end, we first characterize the largest set of arrival processes (rather than rates) whose deadline and delivery ratio requirements can be satisfied. Then, we propose a dynamic (channel) coding algorithm that provably satisfies the requirements of any arrival process in this region. This optimal dynamic algorithm evolves through simple iterations to utilize a combination of pricing and finite-horizon dynamic programming operations. Next, we proposed two low-complexity approximations of the algorithm that has provable performance. We also extend the setup to allow for a flow controller that adjusts the incoming flow rates to satisfy their delivery ratio constraints when the arrival process is unknown but controllable. We propose a joint dynamic coding and a rate control algorithm to solve this problem, and prove its stability under the stochastic system operation. We also apply these general results to an important wireless down-link broadcast scenario with and without random network coding capabilities. Our theoretical work is supported by extensive numerical studies, which also reveal that our dynamic coding strategy outperforms any static coding strategy by opportunistically exploiting the statistical variations in the arrival and channel processes. [ABSTRACT FROM PUBLISHER]

When conducting remote mine-hunting operations with a sidescan-sonar-equipped vehicle, a lawn-mowing search pattern is standard if no prior information on potential target locations is available. Upon completion of this initial search, a list of contacts is obtained. The overall classification performance can be significantly improved by revisiting these contacts to collect additional looks. This paper provides, for the first time, a link between the recent literature which finds optimal secondary looks and optimal route planning software. Automated planning algorithms are needed to generate multiaspect routes to improve the performance of mine-hunting systems and increase the capability of navies to efficiently clear potential mine fields. This paper introduces two new numerical techniques designed to enable current remote mine-hunting systems to achieve secondary paths minimizing the total distance to be traveled and satisfying all motion and imaging constraints. The first “local” approach is based on a sequential algorithm dealing with more tractable subproblems, while the second is “global” and based on simulated annealing. These numerical techniques are applied to two test sites created for the Mongoose sea trial held at the 2007 Autonomous Underwater Vehicle (AUV) Fest, Panama City, FL. Highly satisfactory planning solutions are obtained. [ABSTRACT FROM PUBLISHER]

Particle swarm optimizer (PSO) is a powerful optimization algorithm that has been applied to a variety of problems. It can, however, suffer from premature convergence and slow convergence rate. Motivated by these two problems, a hybrid global optimization strategy combining PSOs with a modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is presented in this paper. The modified BFGS method is integrated into the context of the PSOs to improve the particles' local search ability. In addition, in conjunction with the territory technique, a reposition technique to maintain the diversity of particles is proposed to improve the global search ability of PSOs. One advantage of the hybrid strategy is that it can effectively find multiple local solutions or global solutions to the multimodal functions in a box-constrained space. Based on these local solutions, a reconstruction technique can be adopted to further estimate better solutions. The proposed method is compared with several recently developed optimization algorithms on a set of 20 standard benchmark problems. Experimental results demonstrate that the proposed approach can obtain high-quality solutions on multimodal function optimization problems. [ABSTRACT FROM PUBLISHER]

We propose a new fast algorithm for solving one of the standard approaches to ill-posed linear inverse problems (IPLIP), where a (possibly nonsmooth) regularizer is minimized under the constraint that the solution explains the observations sufficiently well. Although the regularizer and constraint are usually convex, several particular features of these problems (huge dimensionality, nonsmoothness) preclude the use of off-the-shelf optimization tools and have stimulated a considerable amount of research. In this paper, we propose a new efficient algorithm to handle one class of constrained problems (often known as basis pursuit denoising) tailored to image recovery applications. The proposed algorithm, which belongs to the family of augmented Lagrangian methods, can be used to deal with a variety of imaging IPLIP, including deconvolution and reconstruction from compressive observations (such as MRI), using either total-variation or wavelet-based (or, more generally, frame-based) regularization. The proposed algorithm is an instance of the so-called alternating direction method of multipliers, for which convergence sufficient conditions are known; we show that these conditions are satisfied by the proposed algorithm. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is a strong contender for the state-of-the-art. [ABSTRACT FROM AUTHOR]

The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivative-free real parameter optimization algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper analyzes the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. A simple but very useful modification to the classical HS has been proposed in light of the mathematical analysis undertaken here. A comparison with the most recently published variants of HS and four other state-of-the-art optimization algorithms over 15 unconstrained and five constrained benchmark functions reflects the efficiency of the modified HS in terms of final accuracy, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]

An analytical optimization approach of the submersible solenoid valve constrained in a specific small volume is presented. In order to maximum the electromagnetic force, thermal model and electromagnetic model are constructed, and five radial dimensions are optimized when the main limiting quantity is the maximum allowed temperature. A numerical optimization calculation is performed in order to verify the proposed analytical approach. The optimal solution of the numerical calculation is compared with the solution of the analytical approach, which shows a good agreement between the two methods. A thermal analysis of the optimal submersible solenoid valve based on AYSYS is also performed to verify the thermal model. A comparative study between the experimental and simulated static characteristics of the optimal submersible solenoid valve is also performed. The results show that the proposed analytical approach is correct. [ABSTRACT FROM AUTHOR]

We present a method to efficiently compute locally optimal feedback controllers for synthesis problems formulated in the time domain. We minimize a time-domain performance objective subject to state or input time-domain constraints. The possibility to include state or input constraints in the design is very appealing from a practical point of view, in particular for plants subject to operational limits as input saturations. Our method is based on a nonsmooth minimization technique, which can handle time-domain constraints as hard constraints. For model-based designs, a stability constraint can also be handled as a hard constraint. The validity and efficiency of the approach are demonstrated through a variety of numerical tests with comparisons with a state-of-the-art technique in constrained optimization. [ABSTRACT FROM AUTHOR]

In order to find sparse approximations of signals, an appropriate generative model for the signal class has to be known. If the model is unknown, it can be adapted using a set of training samples. This paper presents a novel method for dictionary learning and extends the learning problem by introducing different constraints on the dictionary. The convergence of the proposed method to a fixed point is guaranteed, unless the accumulation points form a continuum. This holds for different sparsity measures. The majorization method is an optimization method that substitutes the original objective function with a surrogate function that is updated in each optimization step. This method has been used successfully in sparse approximation and statistical estimation [e.g., expectation-maximization (EM)] problems. This paper shows that the majorization method can be used for the dictionary learning problem too. The proposed method is compared with other methods on both synthetic and real data and different constraints on the dictionary are compared. Simulations show the advantages of the proposed method over other currently available dictionary learning methods not only in terms of average performance but also in terms of computation time. [ABSTRACT FROM AUTHOR]

Approximate formulations for the 3-dB beamwidth are derived in this letter to measure the spatial resolution of the broadband nonredundant array (NRA) and minimum redundancy array (MRA), which assume the ideal continuous-time, infinite-length filters with the frequency responses obtained by the linearly constrained minimum variance (LCMV) optimization. By these formulations, the beamwidths of NRA and MRA are compared with that of the uniform linear array (ULA). Moreover, the accuracy of the derived formulations is assessed by numerical studies. [ABSTRACT FROM AUTHOR]

In general, decision support is one of the main purposes of model-based analysis of systems. Response surface methodology (RSM) is an optimization technique that has been applied frequently in practice, but few automated variants are currently available. In this paper, we show how to combine RSM with numerical analysis methods to optimize continuous time Markov chain models. Among the many known numerical solution methods for large Markov chains, we consider a Gauss-Seidel solver with relaxation that relies on a hierarchical Kronecker representation as implemented in the APNN Toolbox. To effectively apply RSM for optimizing numerical models, we propose three strategies which are shown to reduce the required number of iterations of the numerical solver. With a set of experiments, we evaluate the proposed strategies with a model of a production line and apply them to optimize a class-based queuing system [ABSTRACT FROM PUBLISHER]

The standard DP (Dynamic Programming) algorithms are limited by the substantial computational demands they put on contemporary serial computers. In this work, the theory behind the solution to serial monadic dynamic programming problems highlights the theory and application of parallel dynamic programming on a general-purpose architecture (Cluster or Network Of Workstations). A simple and well-known technique, message passing, is considered. Several parallel serial monadic DP algorithms are proposed, based on the parallelization in the state variables and the parallelization in the decision variables. Algorithms with no interpolation are also proposed. It is demonstrated how constraints introduce load unbalance which affect scalability and how this problem is inherent to DP. [ABSTRACT FROM AUTHOR]