summary:In this paper, we describe a decomposition algorithm suitable for two-stage convex stochastic programs known as Generalized Benders Decomposition. For this algorithm we propose a new reformulation that incorporates a lower bound cut that serves as a warm-start, decreasing the overall computation time. Additionally, we test the performance of the proposed reformulation on two modifications of the algorithm (bunching and multicut) using numerical examples. The numerical part is programmed in MATLAB and uses state-of-the-art conic solvers
We introduce the two-stage stochastic minimum s − t cut problem. Based on a classical linear 0-1 pro...
Stochastic programming problems have very large dimension and characteristic structures which are tr...
Benders decomposition is a solution method for solving certain large-scale optimization problems. In...
summary:In this paper, we describe a decomposition algorithm suitable for two-stage convex stochasti...
Benders decomposition is one of the most applied methods to solve two-stage stochastic problems (TSS...
This paper introduces a new exact algorithm to solve two-stage stochastic linear programs. Based on ...
We consider two-stage stochastic programming problems with integer recourse. The L-shaped method of ...
This paper focuses on solving two-stage stochastic mixed integer programs (SMIPs) with general mixed...
Benders decomposition entails a two-stage optimization approach to a mixed integer program: first-s...
This paper derives a new splitting-based decomposition algorithm for convex stochastic programs. It ...
Zhao [28] recently showed that the log barrier associated with the recourse function of two-stage st...
We propose a new class of convex approximations for two-stage mixed-integer recourse models, the so-...
This paper considers deterministic global optimization of scenario-based, two-stage stochastic mixed...
International audienceIn Benders decomposition approach to mixed integer programs , the optimization...
The Bachelor thesis is dealing with Benders decomposition in optimization, especially in stochastic ...
We introduce the two-stage stochastic minimum s − t cut problem. Based on a classical linear 0-1 pro...
Stochastic programming problems have very large dimension and characteristic structures which are tr...
Benders decomposition is a solution method for solving certain large-scale optimization problems. In...
summary:In this paper, we describe a decomposition algorithm suitable for two-stage convex stochasti...
Benders decomposition is one of the most applied methods to solve two-stage stochastic problems (TSS...
This paper introduces a new exact algorithm to solve two-stage stochastic linear programs. Based on ...
We consider two-stage stochastic programming problems with integer recourse. The L-shaped method of ...
This paper focuses on solving two-stage stochastic mixed integer programs (SMIPs) with general mixed...
Benders decomposition entails a two-stage optimization approach to a mixed integer program: first-s...
This paper derives a new splitting-based decomposition algorithm for convex stochastic programs. It ...
Zhao [28] recently showed that the log barrier associated with the recourse function of two-stage st...
We propose a new class of convex approximations for two-stage mixed-integer recourse models, the so-...
This paper considers deterministic global optimization of scenario-based, two-stage stochastic mixed...
International audienceIn Benders decomposition approach to mixed integer programs , the optimization...
The Bachelor thesis is dealing with Benders decomposition in optimization, especially in stochastic ...
We introduce the two-stage stochastic minimum s − t cut problem. Based on a classical linear 0-1 pro...
Stochastic programming problems have very large dimension and characteristic structures which are tr...
Benders decomposition is a solution method for solving certain large-scale optimization problems. In...