The set of feasible points of the LMIs is a convex inner approximation of the set of feasible points of the BMI constraints around the current iteration point. Another contribution is the convergence proof of a subsequence of the iterations to a stationary point. Finally, an example of the ...
Application of the sequential parametric convex approximation method to the design of robust trusses Article 01 September 2016 Quadratic Multipoint Exponential Approximation: Surrogate Model for Large-Scale Optimization Chapter © 2018 An Algorithm for Constrained Optimization with Applications to the ...
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. Kernel Density ...
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. Kernel Density ...
This is primarily due to the convenient parametric representation which involves the first two statistical moments of the Gaussians, but other representations are possible. Here, the mean and covariance of the Gaussian mixture are, respectively, the center and shape of the group or extended object....
Robust designSequential parametric convex approximationStress constraintsTruss optimizationWe study an algorithm recently proposed, which is called sequential parametric approximation method, that finds the solution of a differentiable nonconvex optimization problem by solving a sequence of differentiable convex ...
A working-set framework for sequential convex approximation methodsSequential convex approximation methods, Active-set method, Inequality constrained optimizationMathias Stolpe
The overbounding method is essentially the same as the difference of convex optimization approach and the inner approximation method. The unified view of these methods is provided. This paper also proposes a new overbounding algorithm based on the less conservative overbounding ...
non-parametric estimationuniform approximationWe propose a sequential method to estimate monotone convex functions that consists of: (ⅰ) monotone regression via solving a constrained least square (LS) problem and (ⅱ) convexification of the monotone regression estimate via solving a uniform approximation...
In this study, we propose a sequential convex programming (SCP) method that uses an enhanced two-point diagonal quadratic approximation (eTDQA) to generate diagonal Hessian terms of approximate functions. In addition, we use nonlinear programming (NLP) filtering, conservatism, and trust region ...