For many intractable optimization problems efficient approximation algorithms have been developed that return near-optimal solutions. We show how such algorithms and worst-case bounds for the quality of their results can be derived and verified as structured programs. The proposed method has two key steps. First, auxiliary variables are introduced that allow a formal analysis of the worst-case behavior. In a second step these variables are eliminated from the program and existential quantifiers are introduced in assertions. We show that the elimination procedure preserves validity of proofs and illustrate the approach by two examples
We introduce a new framework for designing and analyzing algorithms. Our framework applies best to p...
. In this paper we generalize the notion of polynomial-time approximation scheme preserving reducibi...
An α-approximation algorithm is an algorithm guaranteed to output a solution that is within an α rat...
For many intractable optimization problems efficient approximation algorithms have been developed th...
In this note we discuss some drawbacks of some approaches to the classification of NP-complete optim...
We investigate the relationship between logical expressibility of NP optimization problems and thei...
AbstractWe investigate the relationship between logical expressibility of NP optimization problems a...
AbstractThis paper presents the main results obtained in the field of approximation algorithms in a ...
AbstractWe introduce a formal framework for studying approximation properties of NP optimization (NP...
One can try to parametrize the set of the instances of an optimization prob-lem and look for in poly...
In a combinatorial optimization problem, when given an input instance, one seeks a feasible solution...
Intractability results for optimization problems complement algorithm design techniques by proving w...
We consider the hardness of approximation of optimization problems from the point of view of definab...
. In the past few years, there has been significant progress in our understanding of the extent to w...
Discrete optimization problems are everywhere, from traditional operations research planning problem...
We introduce a new framework for designing and analyzing algorithms. Our framework applies best to p...
. In this paper we generalize the notion of polynomial-time approximation scheme preserving reducibi...
An α-approximation algorithm is an algorithm guaranteed to output a solution that is within an α rat...
For many intractable optimization problems efficient approximation algorithms have been developed th...
In this note we discuss some drawbacks of some approaches to the classification of NP-complete optim...
We investigate the relationship between logical expressibility of NP optimization problems and thei...
AbstractWe investigate the relationship between logical expressibility of NP optimization problems a...
AbstractThis paper presents the main results obtained in the field of approximation algorithms in a ...
AbstractWe introduce a formal framework for studying approximation properties of NP optimization (NP...
One can try to parametrize the set of the instances of an optimization prob-lem and look for in poly...
In a combinatorial optimization problem, when given an input instance, one seeks a feasible solution...
Intractability results for optimization problems complement algorithm design techniques by proving w...
We consider the hardness of approximation of optimization problems from the point of view of definab...
. In the past few years, there has been significant progress in our understanding of the extent to w...
Discrete optimization problems are everywhere, from traditional operations research planning problem...
We introduce a new framework for designing and analyzing algorithms. Our framework applies best to p...
. In this paper we generalize the notion of polynomial-time approximation scheme preserving reducibi...
An α-approximation algorithm is an algorithm guaranteed to output a solution that is within an α rat...