Branch-and-Bound Methods: A Survey

Lawler, Edward E.; Wood, Derek

doi:10.1287/opre.14.4.699

Cited by 1,547 publications

(616 citation statements)

References 29 publications

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“…This approach is not practical for n d larger than around 15 or so, or smaller, if the other dimensions are large. A branch and bound method, or other global optimization technique, can be used to (possibly) speed up computation of the globally optimal solution [14], [15]. But the worst case complexity of any method that computes the global solution is exponential in n d .…”

Section: B) Map Estimationmentioning

confidence: 99%

Mixed linear system estimation and identification

2010

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Abstract-We consider a mixed linear system model, with both continuous and discrete inputs and outputs, described by a coefficient matrix and a set of noise variances. When the discrete inputs and outputs are absent, the model reduces to the usual noise-corrupted linear system. With discrete inputs only, the model has been used in fault estimation, and with discrete outputs only, the system reduces to a probit model. We consider two fundamental problems: Estimating the model input, given the model parameters and the model output; and identifying the model parameters, given a training set of input-output pairs. The estimation problem leads to a mixed Boolean-convex optimization problem, which can be solved exactly when the number of discrete variables is small enough. In other cases the estimation problem can be solved approximately, by solving a convex relaxation, rounding, and possibly, carrying out a local optimization step. The identification problem is convex and so can be exactly solved. Adding 1 regularization to the identification problem allows us to trade off model fit and model parsimony. We illustrate the identification and estimation methods with a numerical example.

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Section: B) Map Estimationmentioning

confidence: 99%

Mixed linear system estimation and identification

2010

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“…To provide optimality, we must complement it with a search strategy. We use best first with branch-and-bound [33], using the cost of the partial configuration as a lower bound and pruning when this exceeds the lowest cost among the complete configurations generated so far. The search terminates when all partial configurations on the stack have a higher cost.…”

Section: Theorem 2 (Completeness) the Configuration Algorithm Eventuamentioning

confidence: 99%

Autonomous functional configuration of a network robot system

Lundh

Karlsson

Saffiotti

2008

Robotics and Autonomous Systems

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We consider distributed systems of networked robots in which: (1) each robot includes sensing, acting and/or processing modular functionalities; and (2) robots can help each other by offering those functionalities. A functional configuration is any way to allocate and connect functionalities among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In this paper, we propose an approach to automatically generate, at run time, a functional configuration of a network robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. Moreover, our configuration planner can be combined with an action planner to deal with tasks that require sequences of configurations. We illustrate our approach on a specific type of network robot system, called Peis-Ecology, and show experiments in which a sequence of configurations is automatically generated and executed on real robots. These experiments demonstrate that our self-configuration approach can help the system to achieve greater autonomy, flexibility and robustness.

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“…We implemented a heuristic discrete optimization algorithm, Branch and Bound [10], to obtain the heuristic optimum disclosure rule. Figure 12 shows that the discrete optimization algorithm is superior in terms of execution time compared to the brute-force algorithm with no significant risk increase.…”

Section: B Experimentsmentioning

confidence: 99%

Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk

Lebanon

Scannapieco

Fouad

et al. 2006

Privacy in Statistical Databases

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An important issue any organization or individual has to face when managing data containing sensitive information, is the risk that can be incurred when releasing such data. Even though data may be sanitized before being released, it is still possible for an adversary to reconstruct the original data using additional information thus resulting in privacy violations. To date, however, a systematic approach to quantify such risks is not available. In this paper we develop a framework, based on statistical decision theory, that assesses the relationship between the disclosed data and the resulting privacy risk. We model the problem of deciding which data to disclose, in terms of deciding which disclosure rule to apply to a database. We assess the privacy risk by taking into account both the entity identification and the sensitivity of the disclosed information. Furthermore, we prove that, under some conditions, the estimated privacy risk is an upper bound on the true privacy risk. Finally, we relate our framework with the k-anonymity disclosure method. The proposed framework makes the assumptions behind k-anonymity explicit, quantifies them, and extends them in several natural directions.

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Branch-and-Bound Methods: A Survey

Cited by 1,547 publications

References 29 publications

Mixed linear system estimation and identification

Mixed linear system estimation and identification

Autonomous functional configuration of a network robot system

Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk

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