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Unformatted text preview: Week 9 Optimization Local extrema Our main goal in optimization of 2variable functions will be to find their local extrema: • f ( x,y ) has a local maximum at ( a,b ) if f ( x,y ) ≤ f ( a,b ) for all ( x,y ) around ( a,b ). • f ( x,y ) has a local minimum at ( a,b ) if f ( x,y ) ≥ f ( a,b ) for all ( x,y ) around ( a,b ). ’Local’ means it is the largest/smallest value ’in a neighborhood’ around ( a,b ); there could be larger/smaller values further away. The largest/smallest value overall is called the abso lute maximum/minimum, but we won’t cover those in this course. To find local extrema, we won’t really use these definitions directly, we will use critical points. Critical Points The point ( a,b ) is called a critical point of f ( x,y ) if f x ( a,b ) = 0 and f y ( a,b ) = 0, or if f x or f y does not exist at ( a,b ). Actually, we won’t see the second type (nonexisting partial derivatives) much in this course, we’ll focus on the type where the partial derivatives are zero. Compare this definition to that for 1variable functions f ( x ): they have a critical point at x = a if f ( a ) = 0 or if f ( a ) does not exist. Just like for 1variable functions, the reason for considering critical points of 2variable functions is this: Fact: If f ( x,y ) has a local maximum or minimum at ( a,b ), then ( a,b ) is a critical point....
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 Fall '10
 MalabikaPramanik
 Calculus, Critical Point, saddle point, fxx

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