A restarting approach for the symmetric rank one update for unconstrained optimization |
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Authors: | Wah June Leong Malik Abu Hassan |
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Institution: | (1) University Putra Malaysia UPM, 43400 Serdang, Selangor, Malaysia |
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Abstract: | Two basic disadvantages of the symmetric rank one (SR1) update are that the SR1 update may not preserve positive definiteness
when starting with a positive definite approximation and the SR1 update can be undefined. A simple remedy to these problems
is to restart the update with the initial approximation, mostly the identity matrix, whenever these difficulties arise. However,
numerical experience shows that restart with the identity matrix is not a good choice. Instead of using the identity matrix
we used a positive multiple of the identity matrix. The used positive scaling factor is the optimal solution of the measure
defined by the problem—maximize the determinant of the update subject to a bound of one on the largest eigenvalue. This measure
is motivated by considering the volume of the symmetric difference of the two ellipsoids, which arise from the current and
updated quadratic models in quasi-Newton methods. A replacement in the form of a positive multiple of the identity matrix
is provided for the SR1 update when it is not positive definite or undefined. Our experiments indicate that with such simple
initial scaling the possibility of an undefined update or the loss of positive definiteness for the SR1 method is avoided
on all iterations. |
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Keywords: | Symmetric rank one Volume of ellipsoid Unconstrained optimization |
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