You can also use the "Lagrange multiplier" method. In order to minimize f(x,y,z), while subject to the condition g(x,y,z)= constant, then the gradients must be in the same direction- \nabla f[\itex] must be a multiple of \nabla g or \nabla f= \lambda\nabla g (\lambda is the "Lagrange multiplier")- so you differentiate <b>both</b> functions. <br />
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Here, f(x,y,z)= 2xy+ 2yz+ 2xz and g(x,y,z)= xyz= 256. \nabla f= (2y+ 2z)\vec{i}+ (2x+2z)\vec{j}+ (2y+ 2x)\vec{k} and \nabla g= yz\vec{i}+ xz\vec{j}+ xy\vec{k} so 2y+ 2z= \lambda yz, 2x+ 2z= \lambda xz, 2y+ 2x= \lambda xy. Since we don't really need to determine \lambda one method I like to solve equations like these is to divide one equation by another: <br />
\frac{2y+ 2z}{2x+ 2z}= \frac{y}{x}<br />
and <br />
\frac{2x+ 2y}{2y+ 2x}= \frac{x}{z}<br />
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Those can be written as y^2+ zy= xy+ yz or y= x and xz+ yz= xy+ x^2 or z= x. That is, x= y= z (which is reasonable from symmetry considerations). Putting x= y= z into xyz= 256, we have x<sup>3</sup>= 256= 2<sup>8</sup>= 4<sup>3</sup>(4) so x= y= z= 4\sqrt[4]{4}.