845 resultados para Constraint solving


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This paper introduces PSOPT, an open source optimal control solver written in C++. PSOPT uses pseudospectral and local discretizations, sparse nonlinear programming, automatic differentiation, and it incorporates automatic scaling and mesh refinement facilities. The software is able to solve complex optimal control problems including multiple phases, delayed differential equations, nonlinear path constraints, interior point constraints, integral constraints, and free initial and/or final times. The software does not require any non-free platform to run, not even the operating system, as it is able to run under Linux. Additionally, the software generates plots as well as LATEX code so that its results can easily be included in publications. An illustrative example is provided.

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We present extensive molecular dynamics simulations of the dynamics of diluted long probe chains entangled with a matrix of shorter chains. The chain lengths of both components are above the entanglement strand length, and the ratio of their lengths is varied over a wide range to cover the crossover from the chain reptation regime to tube Rouse motion regime of the long probe chains. Reducing the matrix chain length results in a faster decay of the dynamic structure factor of the probe chains, in good agreement with recent neutron spin echo experiments. The diffusion of the long chains, measured by the mean square displacements of the monomers and the centers of mass of the chains, demonstrates a systematic speed-up relative to the pure reptation behavior expected for monodisperse melts of sufficiently long polymers. On the other hand, the diffusion of the matrix chains is only weakly perturbed by the diluted long probe chains. The simulation results are qualitatively consistent with the theoretical predictions based on constraint release Rouse model, but a detailed comparison reveals the existence of a broad distribution of the disentanglement rates, which is partly confirmed by an analysis of the packing and diffusion of the matrix chains in the tube region of the probe chains. A coarse-grained simulation model based on the tube Rouse motion model with incorporation of the probability distribution of the tube segment jump rates is developed and shows results qualitatively consistent with the fine scale molecular dynamics simulations. However, we observe a breakdown in the tube Rouse model when the short chain length is decreased to around N-S = 80, which is roughly 3.5 times the entanglement spacing N-e(P) = 23. The location of this transition may be sensitive to the chain bending potential used in our simulations.

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A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

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A chapter outlining a theoretical position on the definition of the speech language disorder, cluttering.

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This paper considers two-stage iterative processes for solving the linear system $Af = b$. The outer iteration is defined by $Mf^{k + 1} = Nf^k + b$, where $M$ is a nonsingular matrix such that $M - N = A$. At each stage $f^{k + 1} $ is computed approximately using an inner iteration process to solve $Mv = Nf^k + b$ for $v$. At the $k$th outer iteration, $p_k $ inner iterations are performed. It is shown that this procedure converges if $p_k \geqq P$ for some $P$ provided that the inner iteration is convergent and that the outer process would converge if $f^{k + 1} $ were determined exactly at every step. Convergence is also proved under more specialized conditions, and for the procedure where $p_k = p$ for all $k$, an estimate for $p$ is obtained which optimizes the convergence rate. Examples are given for systems arising from the numerical solution of elliptic partial differential equations and numerical results are presented.

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Developing brief training interventions that benefit different forms of problem solving is challenging. In earlier research, Chrysikou (2006) showed that engaging in a task requiring generation of alternative uses of common objects improved subsequent insight problem solving. These benefits were attributed to a form of implicit transfer of processing involving enhanced construction of impromptu, on-the-spot or ‘ad hoc’ goal-directed categorizations of the problem elements. Following this, it is predicted that the alternative uses exercise should benefit abilities that govern goal-directed behaviour, such as fluid intelligence and executive functions. Similarly, an indirect intervention – self-affirmation (SA) – that has been shown to enhance cognitive and executive performance after self-regulation challenge and when under stereotype threat, may also increase adaptive goal-directed thinking and likewise should bolster problem-solving performance. In Experiment 1, brief single-session interventions, involving either alternative uses generation or SA, significantly enhanced both subsequent insight and visual–spatial fluid reasoning problem solving. In Experiment 2, we replicated the finding of benefits of both alternative uses generation and SA on subsequent insight problem-solving performance, and demonstrated that the underlying mechanism likely involves improved executive functioning. Even brief cognitive– and social–psychological interventions may substantially bolster different types of problem solving and may exert largely similar facilitatory effects on goal-directed behaviours.

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We show that the four-dimensional variational data assimilation method (4DVar) can be interpreted as a form of Tikhonov regularization, a very familiar method for solving ill-posed inverse problems. It is known from image restoration problems that L1-norm penalty regularization recovers sharp edges in the image more accurately than Tikhonov, or L2-norm, penalty regularization. We apply this idea from stationary inverse problems to 4DVar, a dynamical inverse problem, and give examples for an L1-norm penalty approach and a mixed total variation (TV) L1–L2-norm penalty approach. For problems with model error where sharp fronts are present and the background and observation error covariances are known, the mixed TV L1–L2-norm penalty performs better than either the L1-norm method or the strong constraint 4DVar (L2-norm)method. A strength of the mixed TV L1–L2-norm regularization is that in the case where a simplified form of the background error covariance matrix is used it produces a much more accurate analysis than 4DVar. The method thus has the potential in numerical weather prediction to overcome operational problems with poorly tuned background error covariance matrices.

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In this paper, we propose a new velocity constraint type for Redundant Drive Wire Mechanisms. The purpose of this paper is to demonstrate that the proposed velocity constraint module can fix the orientation of the movable part and to use the kinematical analysis method to obtain the moving direction of the movable part. First, we discuss the necessity of using this velocity constraint type and the possible applications of the proposed mechanism. Second, we derive the basic equations of a wire mechanism with this constraint type. Next, we present a method of motion analysis on active and passive constraint spaces, which is used to find the moving direction of a movable part. Finally, we apply the above analysis method on a wire mechanism with a velocity constraint module and on a wire mechanism with four double actuator modules. By evaluating the results, we prove the validity of the proposed constraint type.

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It is shown that under reasonable assumptions, conservation of angular momentum provides a strong constraint on gravity wave drag feedbacks to radiative perturbations in the middle atmosphere. In the time mean, radiatively induced temperature perturbations above a given altitude z cannot induce changes in zonal mean wind and temperature below z through feedbacks in gravity wave drag alone (assuming an unchanged gravity wave source spectrum). Thus, despite the many uncertainties in the parameterization of gravity wave drag, the role of gravity wave drag in middle-atmosphere climate perturbations may be much more limited than its role in climate itself. This constraint limits the possibilities for downward influence from the mesosphere. In order for a gravity wave drag parameterization to respect the momentum constraint and avoid spurious downward influence, any nonzero parameterized momentum flux at a model lid must be deposited within the model domain, and there must be no zonal mean sponge layer. Examples are provided of how violation of these conditions leads to spurious downward influence. For planetary waves, the momentum constraint does not prohibit downward influence, but it limits the mechanisms by which it can occur: in the time mean, downward influence from a radiative perturbation can only arise through changes in reflection and meridional propagation properties of planetary waves.

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Temporary work has expanded in the last three decades with adverse implications for inequalities. Because temporary workers are a constituency that is unlikely to impose political costs, governments often choose to reduce temporary work regulations. While most European countries have indeed implemented such reforms, France went in the opposite direction, despite having both rigid labour markets and high unemployment. My argument to solve this puzzle is that where replaceability is high, workers in permanent and temporary contracts have overlapping interests, and governments choose to regulate temporary work to protect permanent workers. In turn, replaceability is higher where permanent workers’ skills are general and wage coordination is low. Logistic regression analysis of the determinants of replaceability — and how this affects governments’ reforms of temporary work regulations — supports my argument. Process tracing of French reforms also confirm that the left has tightened temporary work regulations to compensate for the high replaceability.

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It has been reported that the ability to solve syllogisms is highly g-loaded. In the present study, using a self-administered shortened version of a syllogism-solving test, the BAROCO Short, we examined whether robust findings generated by previous research regarding IQ scores were also applicable to BAROCO Short scores. Five syllogism-solving problems were included in a questionnaire as part of a postal survey conducted by the Keio Twin Research Center. Data were collected from 487 pairs of twins (1021 individuals) who were Japanese junior high or high school students (ages 13–18) and from 536 mothers and 431 fathers. Four findings related to IQ were replicated: 1) The mean level increased gradually during adolescence, stayed unchanged from the 30s to the early 50s, and subsequently declined after the late 50s. 2) The scores for both children and parents were predicted by the socioeconomic status of the family. 3) The genetic effect increased, although the shared environmental effect decreased during progression from adolescence to adulthood. 4) Children's scores were genetically correlated with school achievement. These findings further substantiate the close association between syllogistic reasoning ability and g.

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Solving pharmaceutical crystal structures from powder diffraction data is discussed in terms of the methodologies that have been applied and the complexity of the structures that have been solved. The principles underlying these methodologies are summarized and representative examples of polymorph, solvate, salt and cocrystal structure solutions are provided, together with examples of some particularly challenging structure determinations.