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We all want to maximize our gains and minimize our losses, but decisions have uncertain outcomes. What if you could choose between an expected return of $1000 with no chance of losing any amount, or an expected return of $5000 with a chance of losing $50,000. Which would you choose? The answer depends upon how risk-averse you are. Many would happily take(More)
We demonstrate that if A 1 ; :::; A m are symmetric positive semideenite n n matrices with positive deenite sum and A is an arbitrary symmetric n n matrix, then the quality of the semideenite relaxation (P) is not worse than 1 2 ln(2m 2). It is shown that this bound is sharp in order, as far as the dependence on m is concerned, and that a feasible solution(More)
Conic quadratic optimization is the problem of minimizing a linear function subject to the intersection of an affine set and the product of quadratic cones. The problem is a convex optimization problem and has numerous applications in engineering, economics, and other areas of science. Indeed, linear and convex quadratic optimization is a special case.(More)
In this survey we review the many faces of the S-lemma, a result about the cor-rectness of the S-procedure. The basic idea of this widely used method came from control theory but it has important consequences in quadratic and semidefinite optimization, convex geometry and linear algebra as well. These were active research areas, but as there was little(More)
Sensitivity analysis is one of the most interesting and preoccupying areas in optimization. Many attempts are made to investigate the problem's behavior when the input data changes. Usually variation occurs in the right hand side of the constraints and/or the objective function coefficients. Degeneracy of optimal solutions causes considerable difficulties(More)