The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances. However the global k-means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, afte...

We formulate minmax flow problems as a DC. optimization problem. We then apply a DC. primal-dual algorithm to solve the resulting problem. The obtained computational results show that the proposed algorithm is efficient thanks to particular structures of the minmax flow problems.

This paper deals with mainly establishing numerous sets of generalized second-order parametric sufficient optimality conditions for a semiinfinite discrete minmax fractional programming problem, while the results on semiinfinite discrete minmax fractional programming problem are achieved based on some partitioning schemes under various types of generalized second-order (F ,β, φ, ρ, θ, m)-univex...

A number of methods for multiple-objective optimization problems (MOP) give as solution to MOP the set of optimal solutions for some single-objective optimization problems associated with it. Well-known examples of these single-objective optimization problems are the minsum and the minmax. In this note, we propose a new parametric single-objective optimization problem associated with MOP by mea...

In the stabilizing consensus problem each agent of a networked system has an input value and is repeatedly writing output value; it required that eventually all values stabilize to same which, moreover, must be one values. We study this for synchronous model with identical anonymous agents are connected by time-varying topology may join at any time (asynchronous start). Our main result generic ...

This proposal adds a function template (std::minmax) and / or an algorithm (std::minmax_element) to the header , both of which are implicitly present (by two separate calls to std::min and std::max, or std::min_element and std::max_element), but admit a better implementation if the two calls are combined into one. Both additions can be considered independently, although they are stro...

Minmax regret optimization aims at finding robust solutions that perform best in the worst-case, compared to the respective optimum objective value in each scenario. Even for simple uncertainty sets like boxes, most polynomially solvable optimization problems have strongly NP-hard minmax regret counterparts. Thus, heuristics with performance guarantees can potentially be of great value, but onl...

In this paper, we study an optimal location query based on a road network. Specifically, given a road network containing clients and servers, an optimal location query is to find a location on the road network such that when a new server is set up at this location, a certain cost function computed based on the clients and servers (including the new server) is optimized. Two types of cost functi...