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62
Complete search in continuous global optimization and constraint satisfaction, Acta Numerica 13
, 2004
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Lock Allocation
 POPL'07
, 2007
"... We introduce lock allocation, an automatic technique that takes a multithreaded program annotated with atomic sections (that must be executed atomically), and infers a lock assignment from global variables to locks and a lock instrumentation that determines where each lock should be acquired and re ..."
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Cited by 26 (0 self)
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We introduce lock allocation, an automatic technique that takes a multithreaded program annotated with atomic sections (that must be executed atomically), and infers a lock assignment from global variables to locks and a lock instrumentation that determines where each lock should be acquired and released such that the resulting instrumented program is guaranteed to preserve atomicity and deadlock freedom (provided all shared state is accessed only within atomic sections). Our algorithm works in the presence of pointers and procedures, and sets up the lock allocation problem as a 01 ILP which minimizes the conflict cost between atomic sections while simultaneously minimizing the number of locks. We have implemented our algorithm for both C with pthreads and Java, and have applied it to infer locks in 15K lines of AOLserver code. Our automatic allocation produces the same results as hand annotations for most of this code, while solving the optimization instances within a second for most programs.
Noncommercial Software for MixedInteger Linear Programming
, 2004
"... We present an overview of noncommercial software tools for the solution of mixedinteger linear programs (MILPs). We first review solution methodologies for MILPs and then present an overview of the available software, including detailed descriptions of eight software packages available under open s ..."
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Cited by 22 (2 self)
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We present an overview of noncommercial software tools for the solution of mixedinteger linear programs (MILPs). We first review solution methodologies for MILPs and then present an overview of the available software, including detailed descriptions of eight software packages available under open source or other noncommercial licenses. Each package is categorized as a black box solver, a callable library, a solver framework, or some combination of these. The distinguishing features of all eight packages are described. The paper concludes with case studies that illustrate the use of two of the solver frameworks to develop custom solvers for specific problem classes and with benchmarking of the six black box solvers.
Mixed Global Constraints and Inference in Hybrid CLPIP Solvers
, 2001
"... The complementing strengths of Constraint (Logic) Programming (CLP) and Mixed Integer Programming (IP) have recently received signicant attention. Although various optimization and constraint programming packages at a rst glance seem to support mixed models, the modeling and solution techniques ..."
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Cited by 17 (8 self)
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The complementing strengths of Constraint (Logic) Programming (CLP) and Mixed Integer Programming (IP) have recently received signicant attention. Although various optimization and constraint programming packages at a rst glance seem to support mixed models, the modeling and solution techniques encapsulated are still rudimentary. Apart from exchanging bounds for variables and objective, little is known of what constitutes a good hybrid model and how a hybrid solver can utilize the complementary strengths of inference and relaxations. This paper adds to the eld by identifying constraints as the essential link between CLP and IP and introduces an algorithm for bidirectional inference through these constraints. Together with new search strategies for hybrid solvers and cutgenerating mixed global constraints, solution speed is improved over both traditional IP codes and newer mixed solvers. Keywords: Mixed Integer Programming, Constraint Logic Programming, Integration, Mixed Global Contraints, Dynamic Linear Relaxations, Inference. AMS Subject classication: 68N99,68Q99,68T99,90C05,90C11,90C27. 1.
Algorithms for Maximum Independent Set Applied to Map Labelling
, 2000
"... We consider the following map labelling problem: given distinct points p 1 , p 2 , . . . , p n in the plane, and given #, find a maximum cardinality set of pairwise disjoint axisparallel # # squares Q 1 , Q 2 , . . . , Q r . This problem reduces to that of finding a maximum cardinality indepe ..."
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We consider the following map labelling problem: given distinct points p 1 , p 2 , . . . , p n in the plane, and given #, find a maximum cardinality set of pairwise disjoint axisparallel # # squares Q 1 , Q 2 , . . . , Q r . This problem reduces to that of finding a maximum cardinality independent set in an associated graph called the conflict graph. We describe several heuristics for the maximum cardinality independent set problem, some of which use an LP solution as input. Also, we describe a branchandcut algorithm to solve it to optimality. The standard independent set formulation has an inequality for each edge in the conflict graph which ensures that only one of its endpoints can belong to an independent set. To obtain good starting points for our LPbased heuristics and good upper bounds on the optimal value for our branchandcut algorithm we replace this set of inequalities by the set of inequalities describing all maximal cliques in the conflict graph. For this streng...
Path planning of autonomous underwater vehicles (AUVs) for adaptive sampling
, 2005
"... Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constrain ..."
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Cited by 13 (2 self)
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Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic understanding, to improve forecast quality, or to sample regions of high interest. This work provides a new pathplanning scheme for the adaptive sampling problem. We define the pathplanning problem in terms of an optimization framework and propose a method based on mixed integer linear programming (MILP). The mathematical goal is to find the vehicle path that maximizes the line integral of the uncertainty of field estimates along this path. Sampling this path can improve the accuracy of the field estimates the most. While achieving this objective, several constraints must be satisfied and are implemented. They relate to vehicle motion, intervehicle coordination, communication, collision avoidance, etc. The MILP formulation is quite powerful to handle different problem constraints and flexible enough to allow easy extensions of the problem. The formulation covers single and multiplevehicle cases as well as singleand multipleday formulations. The need for a multipleday formulation arises when the ocean sampling mission is optimized for several days ahead. We first introduce the details of the formulation, then elaborate on the objective function and constraints, and finally, present a varied set of examples to illustrate the applicability of the proposed method. Index Terms—Adaptive sampling, Autonomous Ocean Sampling Network (AOSN), autonomous underwater vehicle (AUV), data
Algorithms and software for convex mixed integer nonlinear programs, IMA Volumes
"... Abstract. This paper provides a survey of recent progress and software for solving convex mixed integer nonlinear programs (MINLP)s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have ..."
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Abstract. This paper provides a survey of recent progress and software for solving convex mixed integer nonlinear programs (MINLP)s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have received sustained attention in recent years. By exploiting analogies to wellknown techniques for solving mixed integer linear programs and incorporating these techniques into software, significant improvements have been made in the ability to solve these problems. Key words. Mixed Integer Nonlinear Programming; Branch and Bound; AMS(MOS) subject classifications.
Expressing Special Structures in an Algebraic Modeling Language for Mathematical Programming
, 1995
"... A knowledge of the presence of certain special structures can be advantageous in both the formulation and solution of linear programming problems. Thus it is desirable that linear programming software o#er the option of specifying such structures explicitly. As a step in this direction, we describe ..."
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Cited by 11 (3 self)
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A knowledge of the presence of certain special structures can be advantageous in both the formulation and solution of linear programming problems. Thus it is desirable that linear programming software o#er the option of specifying such structures explicitly. As a step in this direction, we describe extensions to an algebraic modeling language that encompass piecewiselinear, network and related structures. Our emphasis is on the modeling considerations that motivate these extensions, and on the design issues that arise in integrating these extensions with the generalpurpose features of the language. We observe that our extensions sometimes make models faster to translate as well as to solve, and that they permit a "columnwise" formulation of the constraints as an alternative to the "rowwise" formulation most often associated with algebraic languages.
New formulations for optimization under stochastic dominance constraints
 SIAM Journal on Optimization
"... Stochastic dominance constraints allow a decisionmaker to manage risk in an optimization setting by requiring their decision to yield a random outcome which stochastically dominates a reference random outcome. We present new integer and linear programming formulations for optimization under first ..."
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Cited by 10 (1 self)
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Stochastic dominance constraints allow a decisionmaker to manage risk in an optimization setting by requiring their decision to yield a random outcome which stochastically dominates a reference random outcome. We present new integer and linear programming formulations for optimization under first and secondorder stochastic dominance constraints, respectively. These formulations are more compact than existing formulations, and relaxing integrality in the firstorder formulation yields a secondorder formulation, demonstrating the tightness of this formulation. We also present a specialized branching strategy and heuristics which can be used with the new firstorder formulation. Computational tests illustrate the potential benefits of the new formulations.
Global Optimization of Nonconvex Nonlinear Programs Using Parallel Branch and Bound
, 1995
"... A branch and bound algorithm for computing globally optimal solutions to nonconvex nonlinear programs in continuous variables is presented. The algorithm is directly suitable for a wide class of problems arising in chemical engineering design. It can solve problems defined using algebraic functions ..."
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Cited by 10 (0 self)
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A branch and bound algorithm for computing globally optimal solutions to nonconvex nonlinear programs in continuous variables is presented. The algorithm is directly suitable for a wide class of problems arising in chemical engineering design. It can solve problems defined using algebraic functions and twice differentiable transcendental functions, in which finite upper and lower bounds can be placed on each variable. The algorithm uses rectangular partitions of the variable domain and a new bounding program based on convex/concave envelopes and positive definite combinations of quadratic terms. The algorithm is deterministic and obtains convergence with final regions of finite size. The partitioning strategy uses a sensitivity analysis of the bounding program to predict the best variable to split and the split location. Two versions of the algorithm are considered, the first using a local NLP algorithm (MINOS) and the second using a sequence of lower bounding programs in the search fo...