Results 221 to 230 of about 230,053 (267)

The Kantorovich Problem with a Parameter and Density Constraints

Mathematical Notes, 2021
The classical optimal transport problem between two probability measures \(\mu,\nu\) and with cost function \(h\) deals with the minimization of the integral \[ \int_{X\times Y} h(x,y)\sigma(dx\, dy) \] where the minimum is taken with respect to all the couplings \(\sigma \in \Pi(\mu,\nu)\), and the latter is the set of measures on \(X\times Y\) with ...
Bogachev, V. I., Doledenok, A. N., Malofeev, I. I.   +2 more
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Parameters for multiple constraints

Theoretica Chimica Acta, 1968
A simple procedure is outlined, to obtain good initial guesses for the parameters for multiple constraints.
D. P. Chong, Margaret Lowe Benston
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Kantorovich Problems with a Parameter and Density Constraints

Siberian Mathematical Journal, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Shape deformations with meaningful parameters and constraints

Proceedings Shape Modeling Applications, 2004., 2004
The emerged computer-aided industrial design (CAID) systems gain increasing popularity among industrial designers. Despite the large number of deformation techniques already implemented in commercial systems or described in the literature, these techniques still lack in providing designers with meaningful shape deformations tools.
Raluca Dumitrescu, Joris S. M. Vergeest
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Constrained parameter optimisation: equality constraints

Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2002
Several methods have been proposed for handling constraints by evolutionary algorithms for parameter optimisation problems. These methods include those based on penalty functions, preservation of feasibility, decoders, repair algorithms, as well as some hybrid techniques.
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On Constraints in Parameter Estimation and Model Misspecification

2018 21st International Conference on Information Fusion (FUSION), 2018
Under perfect model specification several deterministic (non-Bayesian) parameter bounds have been established, including the Cramer-Rae, Bhattacharyya, and the Barankin bound; where each is known to apply only to estimators sharing the same mean as a function of the true parameter. This requirement of common mean represents a constraint on the class of
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Univalence Constraints on the Schwarz–Christoffel Parameters

SIAM Journal on Mathematical Analysis, 1986
The main result and their significance are very elegantly represented in the author's abstract. ''The Area theorem and coefficient inequalities for univalent functions are used to derive inequality constraints on the accessory parameters in the Schwarz-Christoffel formula for the conformal mapping of the unit disk onto the interior or onto the exterior
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Error models with parameter constraints

International Journal of Control, 1996
This work treats the analysis of two adaptive systems described by error models. The desired but unknown parameters of each adaptive system are, however, not independent. In general, only linear constraints upon these parameters are considered, although a constant but unknown scalar that introduces some non-linearities is acceptable within the given ...
MANUEL A. DUARTE, KUMPATI S. NARENDRA
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PARAMETER ESTIMATION USING KALMAN FILTERS WITH CONSTRAINTS

International Journal of Bifurcation and Chaos, 2006
We suggest incorporating dynamical information such as locations of unstable fixed points into parameter estimation algorithms in order to improve the method of reconstructing dynamics from time series data. We show how the process of reconstruction using nonlinear filters such as the extended Kalman filter can be easily modified to take advantage of ...
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Identification for Parabolic Distributed Parameter Systems with Constraints on the Parameters and the State

SIAM Journal on Control and Optimization, 1995
The Duboviskii-Miljutin theory is used to study identification problems associated to a parabolic distributed parameter system. More precisely, the second order, linear, uniformly parabolic equation \[ \partial_t u= -\partial_i(a_{ij}(x, t) \partial_j u)+ b_i(x, t) \partial_i u+ c(x, t) u= f(x, t), \] \[ u|_{\partial\Omega}= g,\quad u|_{t= 0}= u_0(x ...
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