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Transformation Methods MOM (Method of Multipliers) Study of Engineering Optimization Guanyao Huang RUbiNet - Robust and Ubiquitous Networking Research Group 2010 July 9th
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Introduction Motivation Ill-condition of subproblems in penalty approaches. Method: MOM R is no longer iteratively updated, and are updated. Benefits Contour shape remains the same.
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Motivation Inverse penalty
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Convergence is associated with ever- increasing distortion of the penalty contours, which increases significantly the possibility of failure of the unconstrained search method. The unconstrained search might not be completed successfully.
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Solution Method of centers Which is equivalent to: These parameter-free methods, although attractive on the surface, are exactly equivalent to SUMT with a particular choice of updating rule for R.
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MOM: augment the lagrangian to form an unconstrained function whose minimum is a Kuhn-Tucker point of the original problem Another solution: MOM
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Detailed procedure
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Property: Second order derivative If the constraints are linear
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Link between Lagrange multiplier and KT (1) When iteration terminate? or then: with: The limit points of is KT point
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Link between Lagrange multiplier and KT (2) Lagrange multiplier estimates:
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MOM characteristics We still have the problem of choosing R! (6.3.6)
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Revisit example 6.1
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Variable Bounds Experience: simply set the out-of- bounds variables to their violated bounds simultaneously. An example of MOM: Example 6.4. Some bounds are not linear. DFP: Davidon – Fletcher – Powell
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