A5009130251- Advanced Optimization Algorithms Research
- Formal Methods in Verification
- Constraint Satisfaction and Optimization
- Vehicle Routing Optimization Methods
- Scheduling and Optimization Algorithms
- Complexity and Algorithms in Graphs
- Numerical Methods and Algorithms
- Optimization and Mathematical Programming
- Advanced Control Systems Optimization
- Polynomial and algebraic computation
- Parallel Computing and Optimization Techniques
- Matrix Theory and Algorithms
- Optimization and Packing Problems
- Distributed and Parallel Computing Systems
- Scheduling and Timetabling Solutions
- Metaheuristic Optimization Algorithms Research
- Integrated Energy Systems Optimization
- Logic, programming, and type systems
- Logic, Reasoning, and Knowledge
- Smart Grid Energy Management
- Advanced Multi-Objective Optimization Algorithms
- Advanced Graph Theory Research
- Reservoir Engineering and Simulation Methods
- Process Optimization and Integration
- Optimization and Variational Analysis
Zuse Institute Berlin
2015-2024
HTW Berlin - University of Applied Sciences
2020-2024
Technische Universität Berlin
2008
This paper describes the extensions that were added to constraint integer programming framework SCIP in order enable it solve convex and nonconvex mixed-integer nonlinear programs (MINLPs) global optimality. implements a spatial branch-and-bound algorithm based on linear outer-approximation, which is computed by over- underestimation of functions. An expression graph representation constraints allows for bound tightening, structure analysis, reformulation. Primal heuristics are employed...
Abstract We report on the selection process leading to sixth version of Mixed Integer Programming Library, MIPLIB 2017. Selected from an initial pool 5721 instances, new 2017 collection consists 1065 instances. A subset 240 instances was specially selected for benchmarking solver performance. For first time, these sets were compiled using a data-driven supported by solution sequence mixed integer optimization problems, which encode requirements diversity and balancedness with respect...
The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses enhancements and extensions included in 9.0. updates 9.0 include improved symmetry handling, additions improvements nonlinear handlers primal heuristics, new cut generator two selection schemes, branching rule, LP interface, several bug fixes. also features Rust C++ interfaces SCIP, Python...
The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework . focus this article is on role in supporting research. ’s main design principles are discussed, followed by presentation latest performance improvements and developments version 8.0, which serve both as examples application research tool platform further developments. Furthermore, gives an overview interfaces to other modeling...
We describe an iterative refinement procedure for computing extended-precision or exact solutions to linear programming (LP) problems. Arbitrarily precise can be computed by solving a sequence of closely related LPs with limited-precision arithmetic. The solved share the same constraint matrix as original problem instance and are transformed only modification objective function, right-hand side, variable bounds. Exact computation is used compute store representation problems, numeric LPs. At...
The software and data in this repository are a snapshot of the that were used research reported on paper MIP-DD: Delta Debugging for Mixed Integer Programming Solvers by A. Hoen, D. Kamp, Gleixner.
This paper is concerned with optimal operation of pressurized water supply networks at a fixed point in time. We use mixed-integer nonlinear programming (MINLP) model incorporating both the physical laws and discrete decisions such as switching pumps on off. demonstrate that for instances from our industry partner, these stationary models can be solved to $\epsilon$-global optimality within small running times using problem-specific presolving state-of-the-art MINLP algorithms. <br> In...
.This paper is concerned with the exact solution of mixed-integer programs (MIPs) over rational numbers, i.e., without any roundoff errors and error tolerances. Here, one computational bottleneck that should be avoided whenever possible to employ large-scale symbolic computations. Instead it often use safe directed rounding methods, e.g., generate provably correct dual bounds. In this work, we continue leverage paradigm extend an branch-and-bound framework by separation routines for cutting...
We describe an iterative refinement procedure for computing extended precision or exact solutions to linear programming problems (LPs). Arbitrarily precise can be computed by solving a sequence of closely related LPs with limited arithmetic. The solved share the same constraint matrix as original problem instance and are transformed only modification objective function, right-hand side, variable bounds. Exact computation is used compute store representation problems, while numeric LPs. At...
Abstract The last milestone achievement for the roundoff-error-free solution of general mixed integer programs over rational numbers was a hybrid-precision branch-and-bound algorithm published by Cook, Koch, Steffy, and Wolter in 2013. We describe substantial revision extension this framework that integrates symbolic presolving, features an exact repair step solutions from primal heuristics, employs faster LP solver based on iterative refinement, is able to produce independently verifiable...
Abstract This paper describes the computational challenge developed for a competition held in 2023 $$20{\text {th}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>20</mml:mn> <mml:mtext>th</mml:mtext> </mml:mrow> </mml:math> anniversary of Mixed Integer Programming Workshop. The topic this was reoptimization, also known as warm starting, mixed integer linear optimization problems after slight changes to input data common formulation. accelerate proof...