His major focus has been in the design and analysis of algorithms for discrete optimization problems.

His research interests lie in theoretical computer science, discrete optimization and combinatorics.

Discrete optimization is a branch of optimization in applied mathematics and computer science.

Either continuous or discrete optimization is performed.

He is known for his research in nonlinear discrete optimization and combinatorial optimization.

For NP-complete discrete optimization problems, current research literature includes the following topics:

Two notable branches of discrete optimization are:

"Optimal decision-making in the discrete optimization method" (Magdeburg University).

Several discrete optimization algorithms are proposed in this work that scales gracefully with the number of elements (experiments show results with more than 100,000 variables).

It is very simple compared to most other uses of linear programs in discrete optimization, however it illustrates connections to other concepts.

His research interests include discrete optimization, graph theory, applied discrete mathematics, and applied number theory.

WG 7.4 on Discrete Optimization.

Local search or hill climbing methods for solving discrete optimization problems start from an initial configuration and repeatedly move to an improving neighboring configuration.

Robust Discrete Optimization and Network Flows.

Ant Algorithms for Discrete Optimization.

As opposed to discrete optimization, the variables used in the objective function can assume real values, e.g., values from intervals of the real line.

Robust Discrete Optimization and Its Applications, Kluwer.

Tseng has conducted research primarily in continuous optimization and secondarily in discrete optimization and distributed computation.

The processor used in the D-Wave One code-named "Rainier", performs a single mathematical operation named Discrete optimization.

This is a discrete optimization problem, and computationally infeasible by current combinatorial methods, due to the huge number of permutations and the complexity of each computation.

Ant colony optimization is a widely used algorithm which was inspired by the behaviours of ants, and has been effective solving discrete optimization problems related to swarming.

However, even when all the members are themselves continuous, a model containing one or more special ordered sets becomes a discrete optimization problem requiring a mixed integer optimizer for its solution.

In discrete optimization, a special ordered set (SOS) is an ordered set of variables, used as an additional way to specify integrality conditions in an optimization model.

Ant Algorithms for discrete optimization with Gianni Di Caro and Luca Maria Gambardella, Artificial Life, Vol.

There is a large amount of literature on polynomial-time algorithms for certain special classes of discrete optimization, a considerable amount of it unified by the theory of linear programming.