<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Elijah Polak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A convergent optimization method using pattern search algorithms with adaptive precision simulation</style></title><secondary-title><style face="normal" font="default" size="100%">Building Services Engineering Research and Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2004</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">327-338</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Thermal building simulation programs, such as EnergyPlus, compute numerical approximations to solutions of systems of differential algebraic equations. We show that the exact solutions of these systems are usually smooth in the building design parameters, but that the numerical approximations are usually discontinuous due to adaptive solvers and finite precision computations. If such approximate solutions are used in conjunction with optimization algorithms that depend on smoothness of the cost function, one needs to compute high precision solutions, which can be prohibitively expensive if used for all iterations. For such situations, we have developed an adaptive simulation–precision control algorithm that can be used in conjunction with a family of derivative free optimization algorithms. We present the main ingredients of the composite algorithms, we prove that the resulting composite algorithms construct sequences with stationary accumulation points, and we show by numerical experiments that using coarse approximations in the early iterations can significantly reduce computation time.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Elijah Polak</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Godfried Augenbroe</style></author><author><style face="normal" font="default" size="100%">Jan Hensen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A convergent optimization method using pattern search algorithms with adaptive precision simulation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 8th IBPSA Conference</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">coordinate search</style></keyword><keyword><style  face="normal" font="default" size="100%">direct search</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">hooke–jeeves</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">particle swarm optimization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><pub-location><style face="normal" font="default" size="100%">Eindhoven, Netherlands</style></pub-location><volume><style face="normal" font="default" size="100%">III</style></volume><pages><style face="normal" font="default" size="100%">1393-1400</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In solving optimization problems for building design and control, the cost function is often evaluated using a detailed building simulation program. These programs contain code features that cause the cost function to be discontinuous. Optimization algorithms that require smoothness can fail on such problems. Evaluating the cost function is often so time-consuming that stochastic optimization algorithms are run using only a few simulations, which decreases the probability of getting close to a minimum. To show how applicable direct search, stochastic, and gradient-based optimization algorithms are for solving such optimization problems, we compare the performance of these algorithms in minimizing cost functions with different smoothness. We also explain what causes the large discontinuities in the cost functions.&lt;/p&gt;</style></abstract></record></records></xml>