In practical problem situations data are usually inherently unreliable. A mathematical representation of uncertainty leads to stochastic optimization problems. In this paper the complexity of stochastic combinatorial optimization problems is discussed. Surprisingly, certain stochastic versions of NP-hard determinstic combinatorial problems appear to be solvable in polynomial time
Decision making under uncertainty is an important topic in many Industries, such as telecommunicatio...
We present a probabilistic analysis for a large class of combinatorial optimization problems contain...
The structured singular value μ measures the robustness of uncertain Systems. Numerous researchers o...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
Stochastic programming is the subfield of mathematical programming that considers optimization in th...
Abstract. This paper presents an investigation on the computational complexity of stochastic optimiz...
\u3cp\u3eThis paper presents an investigation on the computational complexity of stochastic optimiza...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
The field of combinatorial optimization under uncertainty has received increasing attention within t...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
Many combinatorial optimization problems arising in real-world applications do not have accurate est...
Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult ...
<p>The focus of this thesis is on the design and analysis of algorithms for basic problems in Stocha...
none3siWe consider stochastic problems in which both the objective function and the feasible set are...
We study the stochastic versions of a broad class of combinatorial problems where the weights of the...
Decision making under uncertainty is an important topic in many Industries, such as telecommunicatio...
We present a probabilistic analysis for a large class of combinatorial optimization problems contain...
The structured singular value μ measures the robustness of uncertain Systems. Numerous researchers o...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
Stochastic programming is the subfield of mathematical programming that considers optimization in th...
Abstract. This paper presents an investigation on the computational complexity of stochastic optimiz...
\u3cp\u3eThis paper presents an investigation on the computational complexity of stochastic optimiza...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
The field of combinatorial optimization under uncertainty has received increasing attention within t...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
Many combinatorial optimization problems arising in real-world applications do not have accurate est...
Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult ...
<p>The focus of this thesis is on the design and analysis of algorithms for basic problems in Stocha...
none3siWe consider stochastic problems in which both the objective function and the feasible set are...
We study the stochastic versions of a broad class of combinatorial problems where the weights of the...
Decision making under uncertainty is an important topic in many Industries, such as telecommunicatio...
We present a probabilistic analysis for a large class of combinatorial optimization problems contain...
The structured singular value μ measures the robustness of uncertain Systems. Numerous researchers o...
DOI
Add to ORKG