We propose a robust optimization (RO) framework that immunizes some of the central linear
algebra problems in the presence of data uncertainty. Namely, we formulate linear systems, …

… In the spirit of linear programming theory (see Smith 1995 and Bertsimas and Popescu
1999), we write the dual of Problem (3) by associating a vector of dual variables y = y0 y1 …

Continuous-time stochastic processes are approximations to physically realizable phenomena.
We quantify one aspect of the approximation errors by characterizing the asymptotic …

… In the present paper, we join Bertsimas and Van Parys (2020) in imposing a ridge regularizer,
… First, we note that Bertsimas and Van Parys (2020)’s algorithm can be improved by setting …

… In fact, we were able to prove this for the special case m = 2 (Bertsimas 1988). For m = 1 this
… Bertsimas shows that if there are I roots of (7), then the waiting time distribution is a mixture …

… While we do not have theoretical evidence on the closeness of the approximation, Bertsimas
and Brown [7] report excellent computational results utilizing Problem (8) for constrained …

We propose a semidefinite optimization approach to the problem of deriving tight moment
inequalities for $P(X\in S)$, for a set S defined by polynomial inequalities and a random vector …

… In [5], Bertsimas and Popescu use these methods to find best possible bounds for pricing
financial derivatives without assuming particular price dynamics. Lasserre [13] and Parrilo [17] …

… The full formulation is provided in Bertsimas and Georghiou (2015), but we repeat the … We
also sought to compare our results with Bertsimas and Georghiou (2015), who use a different …

… Note that all functions introduced in this section are either matrix convex or the trace of a
matrix convex function, and thus supply valid convex relaxations when used as regularizers for …