Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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TL;DR: Numerical results show that over 47% of the total taxi trip mileage may be saved if proper level of incentives are provided and if passengers are matched optimally, and suggest that the heuristic algorithm is capable of solving real-world TGR instances efficiently with good solution quality.
Abstract: Taxi group ride (TGR) is one popular case of taxi ridesharing, where passenger trips with nearby origins and destinations and similar departure time are grouped into a single ride. The study investigates theoretical and practical aspects of TGR implementation in real world. In particular, two essential problems on operation strategy and policy making of TGR are examined. First, we investigate the optimal assignment of a set of passengers for the sake of maximizing total saved travel miles. Second, we analyze different behaviors of passengers and drivers in participating taxi group rides, and explore the best incentives for TGR in order to maximize efficiency under optimal assignment. The optimal assignment is formulated as an integer linear programming problem and is further converted into an equivalent graph problem. While the problem is NP-hard, efficient algorithms are needed for real-world on-line implementations. We develop an exact algorithm and a heuristic algorithm to solve the TGR problem, and compare the results with a bounded-error greedy algorithm. The numerical experiments suggest that the heuristic algorithm is capable of solving real-world TGR instances efficiently with good solution quality. To explore the best incentives for grouped taxi rides, comprehensive numerical experiments are conducted using taxi trip data from New York City (US), Wuhan (China), and Shenzhen (China). Our numerical results show that over 47% of the total taxi trip mileage may be saved if proper level of incentives are provided and if passengers are matched optimally.
89 citations
01 Nov 2011
TL;DR: The application of greedy CS detection algorithms to detect CDMA spread multi-user data in the context of transmission with Code Division Multiple Access (CDMA).
Abstract: A possible future application in communications is the wireless uplink transmission in sensor networks This application is mainly characterized by sporadic transmission over a random access channel Since each sensor has a low activity probability, the signal for Multi-User Detection (MUD) is sparse Compressive Sensing (CS) theory introduces detectors that are able to recover sparse signals reliably When applied to MUD, CS detectors perform a joint detection of both data and user activity This allows for less control signaling, since information about the activity state of each user no longer need to be signaled Additionally, CS detectors are able to reliably detect sparse signals even in under-determined systems In the context of transmission with Code Division Multiple Access (CDMA) this property can be exploited by reducing the length of spreading sequences, which increases the symbol-rate for a given bandwidth, and making the CDMA system overloaded In this paper we introduce the application of greedy CS detection algorithms to detect CDMA spread multi-user data
89 citations
22 Apr 2001
TL;DR: An algorithm based on simulated annealing (SA) for the solution of the resulting problem of location area planning is proposed and the quality of the SA technique is investigated by comparing its results to greedy search and random generation methods.
Abstract: Location area (LA) planning plays an important role in cellular networks because of the trade-off caused by paging and registration signaling. The upper bound on the size of an LA is the service area of a mobile switching center (MSC). In that extreme case, the cost of paging is at its maximum, but no registration is needed. On the other hand, if each cell is an LA, the paging cost is minimal, but the registration cost is the largest. In general, the most important component of these costs is the load on the signaling resources. Between the extremes lie one or more partitions of the MSC service area that minimize the total cost of paging and registration. In this paper, we try to find an optimal method for determining the location areas. For that purpose, we use the available network information to formulate a realistic optimization problem. We propose an algorithm based on simulated annealing (SA) for the solution of the resulting problem. Then, we investigate the quality of the SA technique by comparing its results to greedy search and random generation methods.
89 citations
23 Jul 2013
TL;DR: This work uses a powerful sampling technique to adapt a broad class of greedy algorithms to the MapReduce paradigm, and yields efficient algorithms that run in a logarithmic number of rounds, while obtaining solutions that are arbitrarily close to those produced by the standard sequential greedy algorithm.
Abstract: Greedy algorithms are practitioners' best friends - they are intuitive, simple to implement, and often lead to very good solutions. However, implementing greedy algorithms in a distributed setting is challenging since the greedy choice is inherently sequential, and it is not clear how to take advantage of the extra processing power.Our main result is a powerful sampling technique that aids in parallelization of sequential algorithms. We then show how to use this primitive to adapt a broad class of greedy algorithms to the MapReduce paradigm; this class includes maximum cover and submodular maximization subject to p-system constraints. Our method yields efficient algorithms that run in a logarithmic number of rounds, while obtaining solutions that are arbitrarily close to those produced by the standard sequential greedy algorithm. We begin with algorithms for modular maximization subject to a matroid constraint, and then extend this approach to obtain approximation algorithms for submodular maximization subject to knapsack or p-system constraints. Finally, we empirically validate our algorithms, and show that they achieve the same quality of the solution as standard greedy algorithms but run in a substantially fewer number of rounds.
89 citations
TL;DR: This paper proposes a simultaneous conflict and stitch minimization algorithm with an integer linear programming (ILP) formulation that can reduce 33% of stitches and remove conflicts by 87.6% compared with two phase greedy decomposition.
Abstract: Double patterning lithography (DPL) is considered as a most likely solution for 32 nm/22 nm technology. In DPL, the layout patterns are decomposed into two masks (colors), and manufactured through two exposures and etch steps. If the spacing between two features (polygons) is less than certain minimum coloring distance, they have to be assigned opposite colors. However, a proper coloring is not always feasible because two neighboring patterns within the minimum distance may be in the same mask due to complex pattern configurations. In that case, a feature may need to be split into two parts to resolve the conflict, resulting in stitch insertion which causes yield loss due to overlay and line-end effect. While previous layout decomposition approaches perform coloring and splitting separately, in this paper, we propose a simultaneous conflict and stitch minimization algorithm with an integer linear programming (ILP) formulation. Since ILP is in class NP-hard, the algorithm includes three speed-up techniques: (1) grid merging; (2) independent component computation; and (3) layout partition. In addition, our algorithm can be extended to handle design rules such as overlap margin and minimum width for practical use as well as off-grid layout. Our approach can reduce 33% of stitches and remove conflicts by 87.6% compared with two phase greedy decomposition.
89 citations