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)
We investigate families of quadrics that have fixed intersections with two given hyper-planes. The cases when the two hyperplanes are parallel and when they are nonparallel are discussed. We show that these families can be described with only one parameter. In particular we show how the quadrics are transformed as the parameter changes. This research was… (More)
Detecting infeasibility in conic optimization and providing certificates for infea-sibility pose a bigger challenge than in the linear case due to the lack of strong duality. In this paper we generalize the approximate Farkas lemma of Todd and Ye  from the linear to the general conic setting, and use it to propose stopping criteria for interior point… (More)
We present a strong duality theory for optimization problems over symmetric cones without assuming any constraint qualification. We show important complexity implications of the result to semidefinite and second order conic optimization. The result is an application of Borwein and Wolkowicz's facial reduction procedure to express the minimal cone. We use… (More)
We present two novel applications of symmetries for mixed-integer linear programming. First we propose two variants of a new heuristic to improve the objective value of a feasible solution using symmetries. These heuristics can use either the actual permutations or the orbits of the variables to find better feasible solutions. Then we introduce a new class… (More)
The following question arises in stochastic programming: how can one approximate a noisy convex function with a convex quadratic function that is optimal in some sense. Using several approaches for constructing convex approximations we present some optimization models yielding convex quadratic regressions that are optimal approximations in L 1 , L∞ and L 2… (More)
We consider the complexity of gap-free duals in semidefinite programming. Using the theory of homogeneous cones we provide a new representation of Ramana's gap-free dual and show that the new formulation has a much better complexity than originally proved by Ramana.