We develop an algorithmic theory of convex optimization over discrete sets. Using a combination of algebraic and geometric tools we are able to provide polynomial time algorithms for solving broad classes of convex combinatorial optimization problems and convex integer programming problems in variable dimension. We discuss some of the many applications of this theory including to quadratic prog...

Fractional moments have been investigated by many authors to represent the density of univariate and bivariate random variables in different contexts. Fractional moments are indeed important when the density of the random variable has inverse power-law tails and, consequently, it lacks integer order moments. In this paper, starting from the Mellin transform of the characteristic function and by...

This paper introduces a new family of variable length codes for the integers, initially based on the Goldbach conjecture that every even integer is the sum of two primes. For an even integer we decompose the value into its two constituent primes and encode the ordinal numbers of those primes with an Elias gamma code. The method is then elaborated to handle odd integers. The paper then develops ...

Decomposing tensors into orthogonal factors is a well-known task in statistics, machine learning, and signal processing. We study orthogonal outer product decompositions where the factors in the summands in the decomposition are required to be orthogonal across summands, by relating this orthogonal decomposition to the singular value decompositions of the flattenings. We show that it is a non-t...

This technical report provides accompanying proofs for the paper: Verifying Spatial Properties of Array Computations. We show three properties of the lattice model of the stencil specification language. 1) that it is sound with respect to the equational theory of region specifications; 2) that is it sound with respect to the theory of region approximation; 3) that the inference algorithm is sou...

Multi-objective integer or mixed-integer programming problems typically have disconnected feasible domains, making the task of constructing an approximation Pareto front challenging. The present article shows that certain algorithms were originally devised for continuous can be successfully adapted to approximate integer, and mixed-integer, multi-objective problems. Relationships amongst variou...

Mixed-integer rounding (MIR) is a simple, yet powerful procedure for generating valid inequalities for mixed-integer programs. When used as cutting planes, MIR inequalities are very effective for mixed-integer programming problems with unbounded integer variables. For problems with bounded integer variables, however, cutting planes based on lifting techniques appear to be more effective. This i...

Here we study the discrete lot-sizing problem with an initial stock variable and an associated variable upper bound constraint. This problem is of interest in its own right, and is also a natural relaxation of the constant capacity lot-sizing problem with upper bounds and fixed charges on the stock variables. We show that the convex hull of solutions of the discrete lot-sizing problem is obtain...

Bilevel programming problem is characterized as hierarchical structure, involving two optimization problems at deferent levels. When some variables are restricted into integer set, the problem is very challenging for most canonical optimization approaches. In the present paper, a class of nonlinear mixed-integer bilevel programs is taken into account in which the follower is an integer linear p...