Results 1  10
of
37
SCIP: solving constraint integer programs
, 2009
"... Constraint integer programming (CIP) is a novel paradigm which integrates constraint programming (CP), mixed integer programming (MIP), and satisfiability (SAT) modeling and solving techniques. In this paper we discuss the software framework and solver SCIP (Solving Constraint Integer Programs), wh ..."
Abstract

Cited by 122 (0 self)
 Add to MetaCart
Constraint integer programming (CIP) is a novel paradigm which integrates constraint programming (CP), mixed integer programming (MIP), and satisfiability (SAT) modeling and solving techniques. In this paper we discuss the software framework and solver SCIP (Solving Constraint Integer Programs), which is free for academic and noncommercial use and can be downloaded in source code. This paper gives an overview of the main design concepts of SCIP and how it can be used to solve constraint integer programs. To illustrate the performance and flexibility of SCIP, we apply it to two different problem classes. First, we consider mixed integer programming and show by computational experiments that SCIP is almost competitive to specialized commercial MIP solvers, even though SCIP supports the more general constraint integer programming paradigm. We develop new ingredients that improve current MIP solving technology. As a second application, we employ SCIP to solve chip design verification problems as they arise in the logic design of integrated circuits. This application goes far beyond traditional MIP solving, as it includes several highly nonlinear constraints, which can be handled nicely within the constraint integer programming framework. We show anecdotally how the different solving techniques from MIP, CP, and SAT work together inside SCIP to deal with such constraint classes. Finally, experimental results show that our approach outperforms current stateoftheart techniques for proving the validity of properties on circuits containing arithmetic.
Allocation and scheduling for mpsocs via decomposition and nogood generation
 In Procs. of the 11th Intern. Conference on Principles and Practice of Constraint Programming  CP 2005
, 2005
"... This paper proposes a decomposition approach to the allocation and scheduling of a multitask application on a multiprocessor systemonchip (MPSoCs) [Wolf, 2004]. This is currently one of the most critical problems in electronic design automation for VeryLarge Scale Integrated (VLSI) circuits. Wit ..."
Abstract

Cited by 27 (13 self)
 Add to MetaCart
(Show Context)
This paper proposes a decomposition approach to the allocation and scheduling of a multitask application on a multiprocessor systemonchip (MPSoCs) [Wolf, 2004]. This is currently one of the most critical problems in electronic design automation for VeryLarge Scale Integrated (VLSI) circuits. With the limits of chip integration reaching beyond one billion of elementary devices, current advanced integrated hardware platforms for highend consumer application (e.g. multimediaenabled phones) contain multiple processors and memories, as well as complex onchip interconnects. The hardware resources in these MPSoCs need to be optimally allocated and scheduled under tight throughput constraints when executing a target software workload (e.g. a video decoder). The multiprocessor system
BDDs in a branch and cut framework
 EXPERIMENTAL AND EFFICIENT ALGORITHMS, PROCEEDINGS OF THE 4TH INTERNATIONAL WORKSHOP ON EFFICIENT AND EXPERIMENTAL ALGORITHMS (WEA ’05), VOLUME 3503 OF LECTURE NOTES IN COMPUTER SCIENCE
, 2005
"... Branch & Cut is today’s stateoftheart method to solve 0/1integer linear programs. Important for the success of this method is the generation of strong valid inequalities, which tighten the linear programming relaxation of 0/1IPs and thus allow for early pruning of parts of the search tree ..."
Abstract

Cited by 14 (1 self)
 Add to MetaCart
(Show Context)
Branch & Cut is today’s stateoftheart method to solve 0/1integer linear programs. Important for the success of this method is the generation of strong valid inequalities, which tighten the linear programming relaxation of 0/1IPs and thus allow for early pruning of parts of the search tree. In this paper we present a novel approach to generate valid inequalities for 0/1IPs which is based on Binary Decision Diagrams (BDDs). BDDs are a datastructure which represents 0/1vectors as paths of a certain acyclic graph. They have been successfully applied in computational logic, hardware verification and synthesis. We implemented our BDD cutting plane generator in a branchandcut framework and tested it on several instances of the MAXONES problem and randomly generated 0/1IPs. Our computational results show that we have developed competitive code for these problems, on which stateoftheart MIPsolvers fall short.
ScatterD: Spatial Deployment Optimization with Hybrid Heuristic / Evolutionary Algorithms
"... Distributed realtime and embedded (DRE) systems can be composed of hundreds of software components running across tens or hundreds of networked processors that are physically separated from one another. A key concern in DRE systems is determining the spatial deployment topology, which is how the so ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
(Show Context)
Distributed realtime and embedded (DRE) systems can be composed of hundreds of software components running across tens or hundreds of networked processors that are physically separated from one another. A key concern in DRE systems is determining the spatial deployment topology, which is how the software components map to the underlying hardware components. Optimizations, such as placing software components with highfrequency communications on processors that are closer together, can yield a number of important benefits, such as reduced power consumption due to decreased wireless transmission power required to communicate between the processing nodes. Determining a spatial deployment plan across a series of processors that will minimize power consumption is hard since the spatial deployment plan must respect a combination of realtime scheduling, faulttolerance, resource, and other complex constraints. This paper presents a hybrid heuristic/evolutionary algorithm, called ScatterD, for automatically generating spatial deployment plans that minimize power consumption. This work provides the following contributions to the study of spatial deployment optimization for power consumption minimization: (1) it combines heuristic binpacking with an evolutionary algorithm to produce a hybrid algorithm with excellent deployment derivation capabilities and scalability, (2) it shows how a unique representation of the spatial deployment solution space integrates the heuristic and evolutionary algorithms, and (3) it analyzes the results of experiments performed with data derived from a largescale avionics system that compares ScatterD with other automated deployment techniques. These results show that ScatterD reduces power consumption by between 6 % and 240 % more than standard binpacking, genetic, and particle swarm optimization algorithms. 1
Postponing optimization to speed up maxsat solving
 In 19th International Conference on Principles and Practice of Constraint Programming
, 2013
"... Abstract. maxsat is an optimization version of sat that can represent a wide variety of important optimization problems. A recent approach for solving maxsat is to exploit both a sat solver and a Mixed Integer Programming (mip) solver in a hybrid approach. Each solver generates information used by t ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
(Show Context)
Abstract. maxsat is an optimization version of sat that can represent a wide variety of important optimization problems. A recent approach for solving maxsat is to exploit both a sat solver and a Mixed Integer Programming (mip) solver in a hybrid approach. Each solver generates information used by the other solver in a series of iterations that terminates when an optimal solution is found. Empirical results indicate that a bottleneck in this process is the time required by the mip solver, arising from the large number of times it is invoked. In this paper we present a modified approach that postpones the calls to the mip solver. This involves substituting nonoptimal solutions for the optimal ones computed by the mip solver, whenever possible. We describe the new approach and some different instantiations of it. We perform an extensive empirical evaluation comparing the performance of the resulting solvers with other stateoftheart maxsat solvers. We show that the best performing versions of our approach advance the stateoftheart in maxsat solving. 1
Exploiting the power of MIP solvers in Maxsat
 In Proc. SAT, volume 7962 of LNCS
, 2013
"... Abstract. maxsat is an optimization version of satisfiability. Since many practical problems involve optimization, there are a wide range of potential applications for effective maxsat solvers. In this paper we present an extensive empirical evaluation of a number of maxsat solvers. In addition to t ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
(Show Context)
Abstract. maxsat is an optimization version of satisfiability. Since many practical problems involve optimization, there are a wide range of potential applications for effective maxsat solvers. In this paper we present an extensive empirical evaluation of a number of maxsat solvers. In addition to traditional maxsat solvers, we also evaluate the use of a stateoftheart Mixed Integer Program (mip) solver, cplex, for solving maxsat. mip solvers are the most popular technology for solving optimization problems and are also theoretically more powerful than sat solvers. In fact, we show that cplex is quite effective on a range of maxsat instances. Based on these observations we extend a previously developed hybrid approach for solving maxsat, that utilizes both a sat solver and a mip solver. Our extensions aim to take better advantage of the power of the mip solver. The resulting improved hybrid solver is shown to be quite effective. 1
Optimal Methods for Resource Allocation and Scheduling: a CrossDisciplinary Survey
, 2010
"... Classical scheduling formulations typically assume static resource requirements and focus on deciding when to start the problem activities, so as to optimize some performance metric. In many practical cases, however, the decision maker has the ability to choose the resource assignment as well as th ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
Classical scheduling formulations typically assume static resource requirements and focus on deciding when to start the problem activities, so as to optimize some performance metric. In many practical cases, however, the decision maker has the ability to choose the resource assignment as well as the starting times: this is a farfromtrivial task, with deep implications on the quality of the final schedule. Joint resource assignment and scheduling problems are incredibly challenging from a computational perspective. They have been subject of active research in Constraint Programming (CP) and in Operations Research (OR) for a few decades, with quite difference techniques. Both the approaches report individual successes, but they overall perform equally well or (from a different perspective) equally poorly. In particular, despite the well known effectiveness of global constraints for scheduling, comparable results for joint filtering of assignment and scheduling variables have not yet been achieved. Recently, hybrid approaches have been applied to this class of problems: most of them work by splitting the overall problem into an assignment and a scheduling subparts; those are solved in an iterative and interactive fashion with a mix of CP and
Logicbased MultiObjective Optimization for Restoration Planning
"... Summary. After a disruption in an interconnected set of systems, it is necessary to restore service. This requires the determination of the tasks that need to be undertaken to restore service, and then scheduling those tasks using the available resources. This paper discusses combining mathematical ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Summary. After a disruption in an interconnected set of systems, it is necessary to restore service. This requires the determination of the tasks that need to be undertaken to restore service, and then scheduling those tasks using the available resources. This paper discusses combining mathematical programming and constraint programming into multiple objective restoration planning in order to schedule the tasks that need to be performed. There are three classical objectives involved in scheduling problems: the cost, the tardiness, and the makespan. Efficient solutions for the multiple objective function problem are determined using convex combinations of the classical objectives. For each combination, a mixed integer program is solved using a Benders decomposition approach. The Master Problem assigns tasks to workgroups, and then subproblems schedule the tasks assigned to each workgroup. Hooker has proposed using integer programming to solve the master problem and constraint programming to solve the subproblems, when using one of the classical objective functions. We show that this approach can be successfully generalized to the multiple objective problem. The speed at which a useful set of points on the efficient frontier can be determined should allow the integration of the determination of the tasks to be performed with the evaluation of the various costs of performing those tasks.
Singlefacility scheduling over long time horizons by logicbased benders decomposition
 Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, LNCS 6140
, 2010
"... Abstract. Logicbased Benders decomposition can combine mixed integer programming and constraint programming to solve planning and scheduling problems much faster than either method alone. We find that a similar technique can be beneficial for solving pure scheduling problems as the problem size sca ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Abstract. Logicbased Benders decomposition can combine mixed integer programming and constraint programming to solve planning and scheduling problems much faster than either method alone. We find that a similar technique can be beneficial for solving pure scheduling problems as the problem size scales up. We solve singlefacility nonpreemptive scheduling problems with time windows and long time horizons that are divided into segments separated by shutdown times (such as weekends). The objective is to find feasible solutions, minimize makespan, or minimize total tardiness. 1
Mixed Integer Programming vs. Logicbased Benders Decomposition for Planning and Scheduling ⋆
"... Abstract. A recent paper by Heinz and Beck (CPAIOR 2012) found that mixed integer software has become competitive with or superior to logicbased Benders decomposition for the solution of facility assignment and scheduling problems. Their implementation of Benders differs, however, from that describ ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
Abstract. A recent paper by Heinz and Beck (CPAIOR 2012) found that mixed integer software has become competitive with or superior to logicbased Benders decomposition for the solution of facility assignment and scheduling problems. Their implementation of Benders differs, however, from that described in the literature they cite and therefore results in much slower performance than previously reported. We find that when correctly implemented, the Benders method remains 2 to 3 orders of magnitude faster than the latest commercial mixed integer software on larger instances, thus reversing the conclusion of the earlier paper. 1