Facially Dual Complete (Nice) cones and lexicographic tangents

We study the boundary structure of closed convex cones, with a focus on facially dual complete (nice) cones. These cones form a proper subset of facially exposed convex cones, and they behave well in the context of duality theory for convex optimization. Using the well-known and commonly used concept of tangent cones in nonlinear optimization, we introduce some new notions for exposure of faces of convex sets. Based on these new notions, we obtain a necessary condition and a sufficient condition for a cone to be facially dual complete. In our sufficient condition, we utilize a new notion called lexicographic tangent cones (these are a family of cones obtained from a recursive application of the tangent cone concept). Lexicographic tangent cones are related to Nesterov's lexicographic derivatives and to the notion of subtransversality in the context of variational analysis.Comment: 23 pages (15 pages + appendix), 10 figure

On the spectral structure of Jordan-Kronecker products of symmetric and skew-symmetric matrices

Motivated by the conjectures formulated in 2003 by Tun\c{c}el et al., we study interlacing properties of the eigenvalues of $A\otimes B + B\otimes A$ for pairs of $n$-by-$n$ matrices $A, B$. We prove that for every pair of symmetric matrices (and skew-symmetric matrices) with one of them at most rank two, the \emph{odd spectrum} (those eigenvalues determined by skew-symmetric eigenvectors) of $A\otimes B + B\otimes A$ interlaces its \emph{even spectrum} (those eigenvalues determined by symmetric eigenvectors). Using this result, we also show that when $n \leq 3$, the odd spectrum of $A\otimes B + B\otimes A$ interlaces its even spectrum for every pair $A, B$. The interlacing results also specify the structure of the eigenvectors corresponding to the extreme eigenvalues. In addition, we identify where the conjecture(s) and some interlacing properties hold for a number of structured matrices. We settle the conjectures of Tun\c{c}el et al. and show they fail for some pairs of symmetric matrices $A, B$, when $n\geq 4$ and the ranks of $A$ and $B$ are at least $3$

Primal-Dual Interior-Point Methods for Domain-Driven Formulations

We study infeasible-start primal-dual interior-point methods for convex optimization problems given in a typically natural form we denote as Domain-Driven formulation. Our algorithms extend many advantages of primal-dual interior-point techniques available for conic formulations, such as the current best complexity bounds, and more robust certificates of approximate optimality, unboundedness, and infeasibility, to Domain-Driven formulations. The complexity results are new for the infeasible-start setup used, even in the case of linear programming. In addition to complexity results, our algorithms aim for expanding the applications of, and software for interior-point methods to wider classes of problems beyond optimization over symmetric cones.Comment: 44 pages, 2 figures, to appear in Mathematics of Operations Researc

A Comprehensive Analysis of Polyhedral Lift-and-Project Methods

We consider lift-and-project methods for combinatorial optimization problems and focus mostly on those lift-and-project methods which generate polyhedral relaxations of the convex hull of integer solutions. We introduce many new variants of Sherali--Adams and Bienstock--Zuckerberg operators. These new operators fill the spectrum of polyhedral lift-and-project operators in a way which makes all of them more transparent, easier to relate to each other, and easier to analyze. We provide new techniques to analyze the worst-case performances as well as relative strengths of these operators in a unified way. In particular, using the new techniques and a result of Mathieu and Sinclair from 2009, we prove that the polyhedral Bienstock--Zuckerberg operator requires at least $\sqrt{2n}- \frac{3}{2}$ iterations to compute the matching polytope of the $(2n+1)$-clique. We further prove that the operator requires approximately $\frac{n}{2}$ iterations to reach the stable set polytope of the $n$-clique, if we start with the fractional stable set polytope. Lastly, we show that some of the worst-case instances for the positive semidefinite Lov\'asz--Schrijver lift-and-project operator are also bad instances for the strongest variants of the Sherali--Adams operator with positive semidefinite strengthenings, and discuss some consequences for integrality gaps of convex relaxations

Primal-Dual Entropy Based Interior-Point Algorithms for Linear Optimization

We propose a family of search directions based on primal-dual entropy in the context of interior-point methods for linear optimization. We show that by using entropy based search directions in the predictor step of a predictor-corrector algorithm together with a homogeneous self-dual embedding, we can achieve the current best iteration complexity bound for linear optimization. Then, we focus on some wide neighborhood algorithms and show that in our family of entropy based search directions, we can find the best search direction and step size combination by performing a plane search at each iteration. For this purpose, we propose a heuristic plane search algorithm as well as an exact one. Finally, we perform computational experiments to study the performance of entropy-based search directions in wide neighborhoods of the central path, with and without utilizing the plane search algorithms

Quantum and classical coin-flipping protocols based on bit-commitment and their point games

We focus on a family of quantum coin-flipping protocols based on bit-commitment. We discuss how the semidefinite programming formulations of cheating strategies can be reduced to optimizing a linear combination of fidelity functions over a polytope. These turn out to be much simpler semidefinite programs which can be modelled using second-order cone programming problems. We then use these simplifications to construct their point games as developed by Kitaev. We also study the classical version of these protocols and use linear optimization to formulate optimal cheating strategies. We then construct the point games for the classical protocols as well using the analysis for the quantum case. We discuss the philosophical connections between the classical and quantum protocols and their point games as viewed from optimization theory. In particular, we observe an analogy between a spectrum of physical theories (from classical to quantum) and a spectrum of convex optimization problems (from linear programming to semidefinite programming, through second-order cone programming). In this analogy, classical systems correspond to linear programming problems and the level of quantum features in the system is correlated to the level of sophistication of the semidefinite programming models on the optimization side. Concerning security analysis, we use the classical point games to prove that every classical protocol of this type allows exactly one of the parties to entirely determine the coin-flip. Using the relationships between the quantum and classical protocols, we show that only "classical" protocols can saturate Kitaev's lower bound for strong coin-flipping. Moreover, if the product of Alice and Bob's optimal cheating probabilities is 1/2, then one party can cheat with probability 1. This rules out quantum protocols of this type from attaining the optimal level of security.Comment: 41 pages (plus a 17 page appendix). Comments welcom

A search for quantum coin-flipping protocols using optimization techniques

Coin-flipping is a cryptographic task in which two physically separated, mistrustful parties wish to generate a fair coin-flip by communicating with each other. Chailloux and Kerenidis (2009) designed quantum protocols that guarantee coin-flips with near optimal bias. The probability of any outcome in these protocols is provably at most $1/\sqrt{2} + \delta$ for any given $\delta > 0$. However, no explicit description of these protocols is known, and the number of rounds in the protocols tends to infinity as $\delta$ goes to 0. In fact, the smallest bias achieved by known explicit protocols is $1/4$ (Ambainis, 2001). We take a computational optimization approach, based mostly on convex optimization, to the search for simple and explicit quantum strong coin-flipping protocols. We present a search algorithm to identify protocols with low bias within a natural class, protocols based on bit-commitment (Nayak and Shor, 2003) restricting to commitment states used by Mochon (2005). An analysis of the resulting protocols via semidefinite programs (SDPs) unveils a simple structure. For example, we show that the SDPs reduce to second-order cone programs. We devise novel cheating strategies in the protocol by restricting the semidefinite programs and use the strategies to prune the search. The techniques we develop enable a computational search for protocols given by a mesh over the parameter space. The protocols have up to six rounds of communication, with messages of varying dimension and include the best known explicit protocol (with bias 1/4). We conduct two kinds of search: one for protocols with bias below 0.2499, and one for protocols in the neighbourhood of protocols with bias 1/4. Neither of these searches yields better bias. Based on the mathematical ideas behind the search algorithm, we prove a lower bound on the bias of a class of four-round protocols.Comment: 74 pages (plus 16 page appendix), 27 tables, 3 figures. Comments welcom

Vertices of Spectrahedra arising from the Elliptope, the Theta Body, and Their Relatives

Utilizing dual descriptions of the normal cone of convex optimization problems in conic form, we characterize the vertices of semidefinite representations arising from Lov\'asz theta body, generalizations of the elliptope, and related convex sets. Our results generalize vertex characterizations due to Laurent and Poljak from the 1990's. Our approach also leads us to nice characterizations of strict complementarity and to connections with some of the related literature

Strict Complementarity in MaxCut SDP

The MaxCut SDP is one of the most well-known semidefinite programs, and it has many favorable properties. One of its nicest geometric/duality properties is the fact that the vertices of its feasible region correspond exactly to the cuts of a graph, as proved by Laurent and Poljak in 1995. Recall that a boundary point $x$ of a convex set $C$ is called a vertex of $C$ if the normal cone of $C$ at $x$ is full-dimensional. We study how often strict complementarity holds or fails for the MaxCut SDP when a vertex of the feasible region is optimal, i.e., when the SDP relaxation is tight. While strict complementarity is known to hold when the objective function is in the interior of the normal cone at any vertex, we prove that it fails generically at the boundary of such normal cone. In this regard, the MaxCut SDP displays the nastiest behavior possible for a convex optimization problem. We also study strict complementarity with respect to two classes of objective functions. We show that, when the objective functions are sampled uniformly from the negative semidefinite rank-one matrices in the boundary of the normal cone at any vertex, the probability that strict complementarity holds lies in $(0,1)$. We also extend a construction due to Laurent and Poljak of weighted Laplacian matrices for which strict complementarity fails. Their construction works for complete graphs, and we extend it to cosums of graphs under some mild conditions

Optimization Problems over Unit-Distance Representations of Graphs

We study the relationship between unit-distance representations and Lovasz theta number of graphs, originally established by Lovasz. We derive and prove min-max theorems. This framework allows us to derive a weighted version of the hypersphere number of a graph and a related min-max theorem. Then, we connect to sandwich theorems via graph homomorphisms. We present and study a generalization of the hypersphere number of a graph and the related optimization problems. The generalized problem involves finding the smallest ellipsoid of a given shape which contains a unit-distance representation of the graph. We prove that arbitrary positive semidefinite forms describing the ellipsoids yield NP-hard problems