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Unformatted text preview: Lecture 19: Convex NonSmooth Optimization April 2, 2007 Lecture 19 Outline Convex nonsmooth problems Examples Subgradients and subdifferentials Subgradient properties Operations with subgradients and subdifferentials Convex Optimization 1 Lecture 19 ConvexConstrained Nonsmooth Minimization minimize f ( x ) subject to x C Characteristics : The function f : R n 7 R is convex and possibly nondifferentiable The set C R n is nonempty and convex The optimal value f * is finite Our focus here is nondifferentiability Renewed interest comes from largescale problems and the need for dis tributed computations. Main questions: Where do such problems arise? How do we deal with nondifferentiability? How can we solve them? Convex Optimization 2 Lecture 19 Where they arise Naturally in some applications (comm. nets, data fitting, neuralnets): Leastsquares problems minimize m j =1 k h ( w,x j ) y j k 2 subject to w here ( x j ,y j ) , j = 1 ,...,m are the inputoutput pairs, w are weights (decision variables) to be optimized, h is convex possibly nonsmooth In Lagrangian duality minimize q ( , ) subject to A systematic approach for generating primal optimal bounds A part of some primaldual scheme In (sharp) penalty approaches min x C { f ( x ) + tP ( g ( x )) } where t > is a penalty parameter and the penalty function is P ( u ) = m j =1 max { u j , } or P ( u ) = max { u 1 ,...,u m , } Convex Optimization 3 Lecture 19 Example: Optimization in Network Coding Linear Cost Model minimize ( i,j ) L a ij max s S x s ij subject to max s S x s ij c ij for all ( i,j ) L { j  ( i,j ) L } x s ij { j  ( j,i ) } x s ji = b s i for all i N , s S N is the set of nodes in the communication network S is the set of sessions [a session is a pair of nodes that communicate] L is the set of directed links ( i,j ) denotes a link originating at node i and ending at node j c ij is the communication rate capacity of the link ( i,j ) a ij is the cost for the link ( i,j ) x s ij...
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 Spring '07
 AngeliaNedich

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