AbstractWhile the complexity of min–max and min–max regret versions of most classical combinatorial optimization problems has been thoroughly investigated, there are very few studies about their approximation. For a bounded number of scenarios, we establish general approximation schemes which can be used for min–max and min–max regret versions of some polynomial or pseudo-polynomial problems. Applying these schemes to shortest path, minimum spanning tree, minimum weighted perfect matching on planar graphs, and knapsack problems, we obtain fully polynomial-time approximation schemes with better running times than the ones previously presented in the literature
Using ideas and results from polynomial time approximation and exact computation we design approxima...
Simple, polynomial-time, heuristic algorithms for finding approximate solutions to various polynomia...
AbstractUsing ideas and results from polynomial time approximation and exact computation we design a...
While the complexity of min-max and min-max regret versions of most classical combinatorial optimiza...
This paper investigates, for the first time in the literature, the approximation of min–max (regret)...
We present in this paper general pseudo-polynomial time algorithms to solve min-max and min-max regr...
We present in this paper general pseudo-polynomial time algorithms to solve min-maxand min-max regre...
This paper investigates, for the first time in the literature, the approximation of min-max (regret)...
This is a summary of the most important results presented in the author’s PhD thesis. This thesis, w...
Minmax regret optimization aims at finding robust solutions that perform best in the worst-case, com...
This paper focuses on tractable instances of interval data minmax regret graph problems. More precis...
AbstractThis paper investigates the complexity of the min–max and min–max regret versions of the min...
This paper investigates the complexity of the min–max and min–max regret versions of the min s–t cut...
Abstract Minmax regret optimization aims at finding robust solutions that perform best in the worst-...
La version Working Paper attachée s'intitule "Efficient approximation by "low-complexity" exponentia...
Using ideas and results from polynomial time approximation and exact computation we design approxima...
Simple, polynomial-time, heuristic algorithms for finding approximate solutions to various polynomia...
AbstractUsing ideas and results from polynomial time approximation and exact computation we design a...
While the complexity of min-max and min-max regret versions of most classical combinatorial optimiza...
This paper investigates, for the first time in the literature, the approximation of min–max (regret)...
We present in this paper general pseudo-polynomial time algorithms to solve min-max and min-max regr...
We present in this paper general pseudo-polynomial time algorithms to solve min-maxand min-max regre...
This paper investigates, for the first time in the literature, the approximation of min-max (regret)...
This is a summary of the most important results presented in the author’s PhD thesis. This thesis, w...
Minmax regret optimization aims at finding robust solutions that perform best in the worst-case, com...
This paper focuses on tractable instances of interval data minmax regret graph problems. More precis...
AbstractThis paper investigates the complexity of the min–max and min–max regret versions of the min...
This paper investigates the complexity of the min–max and min–max regret versions of the min s–t cut...
Abstract Minmax regret optimization aims at finding robust solutions that perform best in the worst-...
La version Working Paper attachée s'intitule "Efficient approximation by "low-complexity" exponentia...
Using ideas and results from polynomial time approximation and exact computation we design approxima...
Simple, polynomial-time, heuristic algorithms for finding approximate solutions to various polynomia...
AbstractUsing ideas and results from polynomial time approximation and exact computation we design a...