A Linesearch Algorithm With Memory for Unconstrained Optimization [PDF]
, 1998 This paper considers algorithms for unconstrained nonlinear optimization where the model used by the algorithm to represent the objective function explicitly includes memory of the past iterations. This is intended to make the algorithm less "myopic&
Nicholas I. M. Gould +13 more
core +4 more sources
Testing Unconstrained Optimization Software [PDF]
ACM Transactions on Mathematical Software, 1981 Much of the testing of optimization software is inadequate because the number of test functmns is small or the starting points are close to the solution. In addition, there has been too much emphasm on measurmg the efficmncy of the software and not enough on testing reliability and robustness.
Jorge J. Moré +2 more
openaire +1 more source
Robust optimization in simulation: Taguchi and Krige combined [PDF]
, 2011 Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments.
Jack P. C. Kleijnen +9 more
core +1 more source
On the hardness of quadratic unconstrained binary optimization problems [PDF]
, 2022 We use exact enumeration to characterize the solutions of quadratic unconstrained binary optimization problems of less than 21 variables in terms of their distributions of Hamming distances to close-by solutions.
Michielsen, K. +8 more
core +1 more source
A clustering particle swarm optimizer for dynamic optimization [PDF]
, 2009 This article is posted here with permission of the IEEE - Copyright @ 2009 IEEEIn the real world, many applications are nonstationary optimization problems.
Yang, S +5 more
core +1 more source
Higher-Order Optimality Conditions for Degenerate Unconstrained Optimization Problems
, 2022 In this paper necessary and sufficient conditions of a minimum for the unconstrained degenerate optimization problem are presented. These conditions generalize the well-known optimality conditions.
Zadachyn, Viktor; Department of Information Systems, Simon Kuznets Kharkiv National University of Economics
core +1 more source
Modifications of the Limited Memory BFGS Algorithm for Large-scale Nonlinear Optimization
, 2005 In this paper we present two new numerical methods for unconstrained large-scale optimization. These methods apply update formulae, which are derived by considering different techniques of approximating the objective function.
June, Leong Wah, Hassan, Malik Abu
core +1 more source
LARGE SCALE UNCONSTRAINED OPTIMIZATION
, 2009 This work is a survey on the methods for large scale unconstrained optimization. Besides its own theoretical importance, the growing interest in the last years in solving problems with a larger and larger number of variables are arising very frequently ...
ROMA, Massimo
core +1 more source
A Comparison between Fixed-Basis and Variable-Basis Schemes for Function Approximation and Functional Optimization [PDF]
, 2012 Fixed-basis and variable-basis approximation schemes are compared for the problems of function approximation and functional optimization (also known as infinite programming).
Gnecco, Giorgio +3 more
core +1 more source
Globally convergent algorithms for solving unconstrained optimization problems
, 2013 New algorithms for solving unconstrained optimization problems are presented based on the idea of combining two types of descent directions: the direction of anti-gradient and either the Newton or quasi-Newton directions.
Mammadov, Musa +2 more
core +1 more source