An Adaptive Branch-and-Bound Minimization Method Based on Dynamic Programming

1994 ◽  
pp. 409-432 ◽  
Author(s):  
Sandor Vajda ◽  
Charles Delisi
Keyword(s):  
Dynamic Programming ◽  
Branch And Bound ◽  
Minimization Method

scholarly journals Research on Optimization Algorithm Design Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1991-1992
Keyword(s):  
Dynamic Programming ◽  
State Space ◽  
Branch And Bound ◽  
Optimization Algorithm ◽  
Feasible Solution ◽  
Algorithm Design ◽  
Optimization Techniques ◽  
Greedy Method ◽  
Design Techniques

This paper discusses various optimization algorithm design techniques. So, optimization techniques which are discussed in this paper are greedy method, dynamic programming and branch and bound. Problem comes under optimization are used to find either maximum or minimum. All these techniques we have multiple inputs and some constraints and we have to find feasible solution using these inputs and constraints. In greedy method we follow some predefined method. Using that predefined method, we reach to the solution. On contrary to this in dynamic programming we take decision at every step and in the end we reach to the solution. In branch and bound we create state space tree and explore all possibilities of live node. Based on some constraint we start killing some alive nodes. Here, firstly I will discuss all the design techniques. Then types of problems that can be solved under each design techniques and their time complexities.

A Mixture of Dynamic Programming and Branch-and-Bound for the Subset-Sum Problem

1984 ◽  
Vol 30 (6) ◽  
pp. 765-771 ◽  
Author(s):  
Silvano Martello ◽  
Paolo Toth
Keyword(s):  
Dynamic Programming ◽  
Branch And Bound ◽  
Subset Sum Problem ◽  
Subset Sum

scholarly journals AUG-Segmenter: a user-friendly tool for segmentation of long time series

2010 ◽  
Vol 12 (3) ◽  
pp. 318-328 ◽  
Author(s):  
Abdullah Gedikli ◽  
Hafzullah Aksoy ◽  
N. Erdem Unal
Keyword(s):  
Time Series ◽  
Dynamic Programming ◽  
Linear Regression ◽  
Computer Program ◽  
Branch And Bound ◽  
Real World ◽  
The Third ◽  
Time Series Segmentation ◽  
Long Time ◽  
User Friendly

In this study, three algorithms are presented for time series segmentation. The first algorithm is based on the branch-and-bound approach, the second on the dynamic programming while the third is a modified version of the latter into which the remaining cost concept of the former is introduced. A user-friendly computer program called AUG-Segmenter is developed. Segmentation-by-constant and segmentation-by-linear-regression can be performed by the program. The program is tested on real-world time series of thousands of terms and found useful in performing segmentation satisfactorily and fast.

scholarly journals Branch and Bound Algorithm for Dependency Parsing with Non-local Features

2013 ◽  
Vol 1 ◽  
pp. 37-48 ◽  
Author(s):  
Xian Qian ◽  
Yang Liu
Keyword(s):  
Dynamic Programming ◽  
Computational Complexity ◽  
Branch And Bound ◽  
Graphical Models ◽  
Upper Bound ◽  
Local Features ◽  
Dependency Parsing ◽  
Non Local ◽  
Efficient Decoding ◽  
High Computational Complexity

Graph based dependency parsing is inefficient when handling non-local features due to high computational complexity of inference. In this paper, we proposed an exact and efficient decoding algorithm based on the Branch and Bound (B&B) framework where non-local features are bounded by a linear combination of local features. Dynamic programming is used to search the upper bound. Experiments are conducted on English PTB and Chinese CTB datasets. We achieved competitive Unlabeled Attachment Score (UAS) when no additional resources are available: 93.17% for English and 87.25% for Chinese. Parsing speed is 177 words per second for English and 97 words per second for Chinese. Our algorithm is general and can be adapted to non-projective dependency parsing or other graphical models.

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