<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>A parallel computing scheme for minimizing a class of large scale functions</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">R.</mods:namePart><mods:namePart type="family">Ge</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>This paper gives a parallel computing scheme for minimizing a twice continuously differentiable function with the form ƒf(x) = ∑i = 1mƒi(xi) + ∑i = 1m∑j = 1(j &gt; i)m ƒij(xi, xj),where x = (xT1,…,xTm)T and xi ∈ Rni, ∑mi = 1ni = n, and n a very big number. It is proved that we may use m parallel processors and an iterative procedure to find a minimizer of ƒ(x). The convergence and convergence rate are given under some conditions. The conditions for finding a global minimizer of ƒ(x by using this scheme are given, too. A similar scheme can also be used parallelly to solve a large scale system of nonlinear equations in the similar way. A more general case is also investigated.</mods:abstract><mods:originInfo><mods:dateIssued encoding="iso8601">1989-04</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Elsevier</mods:publisher></mods:originInfo><mods:genre>Article</mods:genre></mods:mods>