01637nas a2200145   4500008004100000245010400041210006900145260001200214300001200226490000700238520111900245100002001364700001801384856008901402       2004                        eng d00aA convergent optimization method using pattern search algorithms with adaptive precision simulation0 aconvergent optimization method using pattern search algorithms w  c11/2004  a327-3380 v253 a<p>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.</p>1 aWetter, Michael1 aPolak, Elijah  uhttps://simulationresearch.lbl.gov/publications/convergent-optimization-method-using01634nas a2200241   4500008004100000245010400041210006900145260002700214300001400241490000800255520083600263653002201099653001801121653002201139653001901161653001701180653003201197100002001229700001801249700002401267700001601291856008501307       2003                        eng d00aA convergent optimization method using pattern search algorithms with adaptive precision simulation0 aconvergent optimization method using pattern search algorithms w  aEindhoven, Netherlands  a1393-14000 vIII3 a<p>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.</p>10acoordinate search10adirect search10agenetic algorithm10ahooke–jeeves10aoptimization10aparticle swarm optimization1 aWetter, Michael1 aPolak, Elijah1 aAugenbroe, Godfried1 aHensen, Jan  uhttps://simulationresearch.lbl.gov/publications/convergent-optimization-method-0