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Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
"... Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object’s location one can take a sliding window approach, but this strongly increases the computational ..."
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Cited by 61 (7 self)
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Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object’s location one can take a sliding window approach, but this strongly increases the computational cost, because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in constrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest neighbor classifiers based on the χ²distance. We demonstrate stateoftheart localization performance of the resulting systems on the
Towards a Taxonomy of Parallel Tabu Search Heuristics
, 1997
"... In this paper we present a classification of parallel tabu search metaheuristics based, on the one hand, on the control and communication strategies used in the design of the parallel tabu search procedures and, on the other hand, on how the search space is partitionned. These criteria are then used ..."
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Cited by 51 (13 self)
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In this paper we present a classification of parallel tabu search metaheuristics based, on the one hand, on the control and communication strategies used in the design of the parallel tabu search procedures and, on the other hand, on how the search space is partitionned. These criteria are then used to review the parallel tabu search implementations described in the literature. The taxonomy is further illustrated by the results of several parallelization implementations of a tabu search procedure for multicommodity locationallocation problems with balancing requirements. Key words: Tabu search metaheuristics, Parallelization strategies, Taxonomy R'esum'e Nous pr'esentons un sch'ema de classification des algorithmes parall`eles de recherche avec tabous. La taxonomie est bas'ee, d'une part, sur les strat'egies de controle et de communication des algorithmes parall`eles de recherche avec tabous et, d'autre part, sur les r`egles de partitionnement du domaine. Ces crit`eres sont ensuite...
Parallelization of the Vehicle Routing Problem with Time Windows
, 2001
"... Routing with time windows (VRPTW) has been an area of research that have
attracted many researchers within the last 10 { 15 years. In this period a number
of papers and technical reports have been published on the exact solution of the
VRPTW.
The VRPTW is a generalization of the wellknown capacitat ..."
Abstract

Cited by 27 (1 self)
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Routing with time windows (VRPTW) has been an area of research that have
attracted many researchers within the last 10 { 15 years. In this period a number
of papers and technical reports have been published on the exact solution of the
VRPTW.
The VRPTW is a generalization of the wellknown capacitated routing problem
(VRP or CVRP). In the VRP a
eet of vehicles must visit (service) a number
of customers. All vehicles start and end at the depot. For each pair of customers
or customer and depot there is a cost. The cost denotes how much is costs a
vehicle to drive from one customer to another. Every customer must be visited
exactly ones. Additionally each customer demands a certain quantity of goods
delivered (know as the customer demand). For the vehicles we have an upper
limit on the amount of goods that can be carried (known as the capacity). In
the most basic case all vehicles are of the same type and hence have the same
capacity. The problem is now for a given scenario to plan routes for the vehicles
in accordance with the mentioned constraints such that the cost accumulated
on the routes, the #12;xed costs (how much does it cost to maintain a vehicle) or
a combination hereof is minimized.
In the more general VRPTW each customer has a time window, and between
all pairs of customers or a customer and the depot we have a travel time. The
vehicles now have to comply with the additional constraint that servicing of the
customers can only be started within the time windows of the customers. It
is legal to arrive before a time window \opens" but the vehicle must wait and
service will not start until the time window of the customer actually opens.
For solving the problem exactly 4 general types of solution methods have
evolved in the literature: dynamic programming, DantzigWolfe (column generation),
Lagrange decomposition and solving the classical model formulation
directly.
Presently the algorithms that uses DantzigWolfe given the best results
(Desrochers, Desrosiers and Solomon, and Kohl), but the Ph.D. thesis of Kontoravdis
shows promising results for using the classical model formulation directly.
In this Ph.D. project we have used the DantzigWolfe method. In the
DantzigWolfe method the problem is split into two problems: a \master problem"
and a \subproblem". The master problem is a relaxed set partitioning
v
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problem that guarantees that each customer is visited exactly ones, while the
subproblem is a shortest path problem with additional constraints (capacity and
time window). Using the master problem the reduced costs are computed for
each arc, and these costs are then used in the subproblem in order to generate
routes from the depot and back to the depot again. The best (improving) routes
are then returned to the master problem and entered into the relaxed set partitioning
problem. As the set partitioning problem is relaxed by removing the
integer constraints the solution is seldomly integral therefore the DantzigWolfe
method is embedded in a separationbased solutiontechnique.
In this Ph.D. project we have been trying to exploit structural properties in
order to speed up execution times, and we have been using parallel computers
to be able to solve problems faster or solve larger problems.
The thesis starts with a review of previous work within the #12;eld of VRPTW
both with respect to heuristic solution methods and exact (optimal) methods.
Through a series of experimental tests we seek to de#12;ne and examine a number
of structural characteristics.
The #12;rst series of tests examine the use of dividing time windows as the
branching principle in the separationbased solutiontechnique. Instead of using
the methods previously described in the literature for dividing a problem into
smaller problems we use a methods developed for a variant of the VRPTW. The
results are unfortunately not positive.
Instead of dividing a problem into two smaller problems and try to solve
these we can try to get an integer solution without having to branch. A cut is an
inequality that separates the (nonintegral) optimal solution from all the integer
solutions. By #12;nding and inserting cuts we can try to avoid branching. For the
VRPTW Kohl has developed the 2path cuts. In the separationalgorithm for
detecting 2path cuts a number of test are made. By structuring the order in
which we try to generate cuts we achieved very positive results.
In the DantzigWolfe process a large number of columns may be generated,
but a signi#12;cant fraction of the columns introduced will not be interesting with
respect to the master problem. It is a priori not possible to determine which
columns are attractive and which are not, but if a column does not become part
of the basis of the relaxed set partitioning problem we consider it to be of no
bene#12;t for the solution process. These columns are subsequently removed from
the master problem. Experiments demonstrate a signi#12;cant cut of the running
time.
Positive results were also achieved by stopping the routegeneration process
prematurely in the case of timeconsuming shortest path computations. Often
this leads to stopping the shortest path subroutine in cases where the information
(from the dual variables) leads to \bad" routes. The premature exit
from the shortest path subroutine restricts the generation of \bad" routes signi
#12;cantly. This produces very good results and has made it possible to solve
problem instances not solved to optimality before.
The parallel algorithm is based upon the sequential DantzigWolfe based
algorithm developed earlier in the project. In an initial (sequential) phase unsolved
problems are generated and when there are unsolved problems enough
vii
to start work on every processor the parallel solution phase is initiated. In the
parallel phase each processor runs the sequential algorithm. To get a good workload
a strategy based on balancing the load between neighbouring processors is
implemented. The resulting algorithm is eÆcient and capable of attaining good
speedup values. The loadbalancing strategy shows an even distribution of work
among the processors. Due to the large demand for using the IBM SP2 parallel
computer at UNI#15;C it has unfortunately not be possible to run as many tests
as we would have liked. We have although managed to solve one problem not
solved before using our parallel algorithm.
FATCOP 2.0: Advanced Features in an Opportunistic Mixed Integer Programming Solver
"... We describe FATCOP 2.0, a new parallel mixed integer program solver that works in an opportunistic computing environment provided by the Condor resource management system. We outline changes to the search strategy of FATCOP 1.0 that are necessary to improve resource utilization, together with new te ..."
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Cited by 26 (10 self)
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We describe FATCOP 2.0, a new parallel mixed integer program solver that works in an opportunistic computing environment provided by the Condor resource management system. We outline changes to the search strategy of FATCOP 1.0 that are necessary to improve resource utilization, together with new techniques to exploit heterogeneous resources. We detail several advanced features in the code that are necessary for successful solution of a variety of mixed integer test problems, along with the different usage schemes that are pertinent to our particular computing environment. Computational results demonstrating the effects of the changes are provided and used to generate effective default strategies for the FATCOP solver.
A Parallel GRASP Implementation for the Quadratic Assignment Problem
 Parallel Algorithms for Irregularly Structured Problems – Irregular’94
, 1995
"... In this paper we present a parallel implementation of a Greedy Randomized Adaptive Search Procedure (grasp) for finding approximate solutions to the quadratic assignment problem. In particular, we discuss efficient techniques for largescale sparse quadratic assignment problems on an MIMD parallel c ..."
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Cited by 23 (14 self)
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In this paper we present a parallel implementation of a Greedy Randomized Adaptive Search Procedure (grasp) for finding approximate solutions to the quadratic assignment problem. In particular, we discuss efficient techniques for largescale sparse quadratic assignment problems on an MIMD parallel computer. We report computational experience on a collection of quadratic assignment problems. The code was run on a Kendall Square Research KSR1 parallel computer, using 1, 4, 14, 24, 34, 44, 54, and 64 processors, and achieves an average speedup that is almost linear in the number of processors. 1 Introduction Nonlinear assignment problems, such as quadratic, cubic, and Nadic assignment problems were formulated by Lawler [11]. One of the most extensively studied nonlinear assignment problems is the quadratic assignment problem (QAP). The QAP was first introduced by Koopmans and Beckmann in 1957 as a mathematical model for locating a set of indivisible economic activities [9]. Consider th...
FATCOP: A fault tolerant CondorPVM mixed integer program solver. Mathematical Programming
, 1999
"... Abstract. We describe FATCOP, a new parallel mixed integer program solver written in PVM. The implementation uses the Condor resource management system to provide a virtual machine composed of otherwise idle computers. The solver differs from previous parallel branchandbound codes by implementing ..."
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Cited by 23 (4 self)
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Abstract. We describe FATCOP, a new parallel mixed integer program solver written in PVM. The implementation uses the Condor resource management system to provide a virtual machine composed of otherwise idle computers. The solver differs from previous parallel branchandbound codes by implementing a general purpose parallel mixed integer programming algorithm in an opportunistic multiple processor environment, as opposed to a conventional dedicated environment. It shows how to make effective use of resources as they become available while ensuring the program tolerates resource retreat. The solver performs well on test problems arising from real applications and is particularly useful for solving long running hard mixed integer programming problems.
On the Best Search Strategy in Parallel BranchandBound  BestFirstSearch vs. Lazy DepthFirstSearch.
 Annals of Operations Research
, 1996
"... or because pruning and evaluation tests are more effective in DFS due to the presence of better incumbents. 1 Introduction. One of the key issues of searchbased algorithms in general and B&Balgorithms in particular is the search strategy employed: In which order should the unexplored parts ..."
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Cited by 21 (4 self)
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or because pruning and evaluation tests are more effective in DFS due to the presence of better incumbents. 1 Introduction. One of the key issues of searchbased algorithms in general and B&Balgorithms in particular is the search strategy employed: In which order should the unexplored parts of the solution space be searched? Different search strategies have different properties regarding time efficiency and memory consumption, both when considered in a sequential and a parallel setting. Supported by the EU HCM project SCOOP and the Danish NSF project EPOS M. Perregaard and J. Clausen / Search Strategies in Parallel Branch and Bound 2 In parallel B&B one often regards the BestFirstSearch strategy (BeFS) and the DepthFirstSearch strategy (DFS) to be two of the prime candidates  BeFS due to expectations of efficiency and theoretical properties regarding anomalies, and DFS for reasons of space efficiency. However BeFS requires that the bou
Cooperative Parallel Tabu Search for Capacitated Network Design.” Publication CRT9871, Centre de recherche sur les transports, Université de Montréal
, 1998
"... Centre de recherche sur les transports; Département d’informatique et de recherche opérationnelle, Université ..."
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Cited by 18 (10 self)
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Centre de recherche sur les transports; Département d’informatique et de recherche opérationnelle, Université
Progress in linear programmingbased algorithms for integer programming: An exposition
 INFORMS JOURNAL ON COMPUTING
, 2000
"... This paper is about modeling and solving mixed integer programming (MIP) problems. In the last decade, the use of mixed integer programming models has increased dramatically. Fifteen years ago, mainframe computers were required to solve problems with a hundred integer variables. Now it is possible t ..."
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This paper is about modeling and solving mixed integer programming (MIP) problems. In the last decade, the use of mixed integer programming models has increased dramatically. Fifteen years ago, mainframe computers were required to solve problems with a hundred integer variables. Now it is possible to solve problems with thousands of integer variables on a personal computer and obtain provably good approximate solutions to problems such as set partitioning with millions of binary variables. These advances have been made possible by developments in modeling, algorithms, software, and hardware. This paper focuses on effective modeling, preprocessing, and the methodologies of branchandcut and branchandprice, which are the techniques that make it possible to treat problems with either a very large number of constraints or a very large number of variables. We show how these techniques are useful