- Referenced in 44 articles
- provably optimal worst-case rate of convergence for smooth strongly convex functions. We demonstrate ... efficiency of the proposed algorithm in numerical experiments and examples from image processing...
- Referenced in 157 articles
- topology optimization, polygonal discretizations have been shown not to be susceptible to numerical instabilities such ... used for its discretization. The signed distance function provides all the essential information about...
- Referenced in 76 articles
- algorithm for solving large-scale convex-constrained optimization problems. It combines the classical projected gradient ... objective function and gradient values, and projections onto the feasible set. Some recent numerical tests...
- Referenced in 50 articles
- include rootfinding and optimization solvers, a integrated set of routines for function approximation using ... polynomial, splines and other functional families, a set of numerical integration routines for general functions...
- Referenced in 16 articles
- extensive numerical comparison with functions from the Matlab Optimization Toolbox is carried...
- Referenced in 31 articles
- with a mathematical programming optimization procedure based on a penalty function approach, to impose discrete ... several other numerical techniques (notably a heuristically based combinatorial optimization procedure) to provide an efficient...
- Referenced in 3 articles
- subroutine that calculates the log-likelihood function. Numerical optimization is then used to estimate...
- Referenced in 25 articles
- well suited for optimizing noisy objective functions. The number of function evaluations required for convergence ... Rowan for his Ph.D. Thesis: Functional Stability Analysis of Numerical Algorithms (University of Texas ... algorithm well suited for optimization of high-dimensional noisy functions...
- Referenced in 46 articles
- nonlinear function subject to nonlinear constraints. The field of global optimization is the study ... global optima to optimization problems. Numerica is modeling language for global optimization that makes ... based on a combination of traditional numerical methods such as interval and local methods ... comprehensive presentation of Numerica describes its design, functions, and implementation. It also discusses...
- Referenced in 43 articles
- gradient method: Universal gradient methods for convex optimization problems. In this paper, we present ... actual level of smoothness of the objective function. Their only essential input parameter ... numerical experiments, which demonstrate that the fast rate of convergence, typical for the smooth optimization...
Complex Optimization Toolbox
- Referenced in 30 articles
- Included are generalized algorithms for unconstrained nonlinear optimization: nonlinear conjugate gradient and limited-memory BFGS ... vector-, matrix- or tensor-valued residual functions, complex bound constraints, Levenberg–Marquardt and Gauss–Newton ... dogleg trust region, and much more: automated numerical real and complex differentiation, preservation of unknowns ... Optimization Toolbox. Alternatively, see the toolbox’s Contents.m for an overview of its functionality...
- Referenced in 39 articles
- optimization of computationally expensive functions. A trust-region framework using interpolating Radial Basis Function ... allow ORBIT to interpolate nonlinear functions using fewer function evaluations than the polynomial models considered ... method for adding additional points. We present numerical results on test problems to motivate ... only a relatively small number of expensive function evaluations are available. Results on two very...
- Referenced in 36 articles
- defined functions for application to optimal value functions. The formulation we develop is well suited ... evaluation of first and second derivatives of optimal values. The result is a method that ... code. This provides motivation for the coding numerical routines where the floating point type...
- Referenced in 30 articles
- derivative-free algorithm for bound constrained optimization. We propose a new globally convergent derivative-free ... minimization of a continuously differentiable function in the case that some ... investigates the local behaviour of the objective function on the feasible set by sampling ... noisy functions. Finally, we report the results of a preliminary numerical experience...
- Referenced in 14 articles
- numerical optimizer. It enables exploration of matrix algebra through a variety of operations and functions...
- Referenced in 10 articles
- QuEST function analytically, which is necessary for numerical inversion via a nonlinear optimizer. Monte Carlo...
Library of Triangulations
- Referenced in 14 articles
- optimal Morse vector, i.e., the function is perfect. This is illustrated by numerous computer experiments...
- Referenced in 5 articles
- package smoof: Single and Multi-Objective Optimization Test Functions. Provides generators for a high number ... objective test functions which are frequently used for the benchmarking of (numerical) optimization algorithms. Moreover...
- Referenced in 42 articles
- involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables ... hand, none of the current general nonsmooth optimization methods is efficient in large-scale settings ... based bundle method for nonsmooth large-scale optimization. In addition, we introduce ... Finally, we give some encouraging results from numerical experiments using both academic and practical test...
- Referenced in 34 articles
- tools to fit models to data, optimize any function of the model, perform metabolic control ... combined with a set of sophisticated numerical algorithms that assure the results are obtained fast...