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Parameterized Complexity of Cardinality Constrained Optimization Problems

L. Cai
2007 Computer journal  
We study the parameterized complexity of cardinality constrained optimization problems, i.e. optimization problems that require their solutions to contain specified numbers of elements to optimize solution  ...  Furthermore, we obtain faster exact algorithms for several cardinality constrained optimization problems by transforming them into problems of finding maximum (minimum) weight triangles in weighted graphs  ...  , 2000, for fruitful discussions on the parameterized complexity of cardinality constrained optimization problems, and also to a referee for constructive suggestions.  ... 
doi:10.1093/comjnl/bxm086 fatcat:ibebmrj5nrchlajzno676wcety

Hybrid search for cardinality constrained portfolio optimization

Miguel A. Gomez, Carmen X. Flores, Maria A. Osorio
2006 Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06  
problems.  ...  quadratic programming model is considerably more difficult to solve than the original model; but some computational experiments have shown that hybrid heuristics offer a good alternative for these types of  ...  INTRODUCTION The problem of optimally selecting a portfolio among n assets was formulated by Markowitz in 1952 as a constrained quadratic minimization problem [7] .  ... 
doi:10.1145/1143997.1144302 dblp:conf/gecco/GomezFO06 fatcat:kjgvggiucrglxcsctpu7ehlhfy

Query optimizers

Surajit Chaudhuri
2009 Proceedings of the 35th SIGMOD international conference on Management of data - SIGMOD '09  
Query Optimization is expected to produce good execution plans for complex queries while taking relatively small optimization time.  ...  We argue that it is worth rethinking this prevalent model of the optimizer. Specifically, we discuss how the optimizer may benefit from leveraging rich usage data and from application input.  ...  Once such plans are identified, the DBA can use the Fixed Plan Set Specification directive to constrain the optimizer to always pick the lowest cost plan among these plans for any instance of that parameterized  ... 
doi:10.1145/1559845.1559955 dblp:conf/sigmod/Chaudhuri09 fatcat:akidfurqxbg3fjelwoihhx4peu

Automatic robust convex programming

Johan Löfberg
2012 Optimization Methods and Software  
While introducing the software package, a brief summary of robust optimization is given, as well as some comments on modeling and tractability of complex convex uncertain optimization problems.  ...  This paper presents the robust optimization framework in the modeling language YALMIP, which carries out robust modeling and uncertainty elimination automatically, and allows the user to concentrate on  ...  the cost of increased problem complexity.  ... 
doi:10.1080/10556788.2010.517532 fatcat:4uvmsmjuejflzc6xxidjmkeikq

Tree-Width and the Computational Complexity of MAP Approximations in Bayesian Networks

Johan Kwisthout
2015 The Journal of Artificial Intelligence Research  
The problem of finding the most probable explanation to a designated set of variables given partial evidence (the MAP problem) is a notoriously intractable problem in Bayesian networks, both to compute  ...  We introduce the complexity class FERT, analogous to the class FTP, to capture this notion of fixed-parameter expectation-approximability.  ...  Acknowledgements A previous version of this paper (Kwisthout, 2014) was published in the Proceedings of the Seventh European Workshop on Probabilistic Graphical Models (PGM 2014).  ... 
doi:10.1613/jair.4794 fatcat:6z4vmygryfhudf46rmfhlrb67i

A General Method for Feature Matching and Model Extraction [chapter]

Clark F. Olson
2000 Lecture Notes in Computer Science  
Hough transform variations are somewhat less sensitive the noise, but implementations for complex problems suer from large time and space requirements and the detection of false positives.  ...  An important component of the method is the subdivision of the problem into many subproblems.  ...  Portions of this research were carried out while the author was with Cornell University and the University of California at Berkeley.  ... 
doi:10.1007/3-540-44480-7_2 fatcat:5vci55uqqjguhmsuusl4hkiqba

Spectral clustering with imbalanced data

Jing Qian, Venkatesh Saligrama
2014 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Our approach parameterizes a family of graphs by adaptively modulating node degrees on a fixed node set, to yield a set of parameter dependent cuts reflecting varying levels of imbalance.  ...  The solution to our problem is then obtained by optimizing over these parameters. We present asymptotic limit cut analysis to justify our approach.  ...  So if totally D graphs are parameterized; complexity of learning algorithm is T , the time complexity is O(D(dn 2 logn + T )).  ... 
doi:10.1109/icassp.2014.6854162 dblp:conf/icassp/QianS14 fatcat:3hfnckrhqbgdfmhi2kzjwyvosi

Page 8110 of Mathematical Reviews Vol. , Issue 2004j [page]

2004 Mathematical Reviews  
Zeilfelder, Optimal approx- imation order of interpolation by cubic spline surfaces (255-264); Géza Kos and Tamas Varady, Parameterizing complex triangular meshes (265-274); U. Labsik and G.  ...  -354); Alla Sheffer, Non-optimal parameterizations and user control (355-364); Alon Spira and Ron Kimmel, Geodesic curva- ture flow on parametric surfaces (365-373); G.  ... 

Dynamic Submodular Maximization

Morteza Monemizadeh
2020 Neural Information Processing Systems  
One of the basic primitives in the class of submodular optimization problems is the submodular maximization under a cardinality constraint.  ...  Given a stream of inserts and deletes of elements of an underlying ground set V , we develop a dynamic algorithm that with high probability, maintains a ( 1 2 − )approximation of a cardinality-constrained  ...  Submodular functions have plenty of applications in machine learning and combinatorial optimization and we believe researchers in both these areas would benefit from this simple algorithm.  ... 
dblp:conf/nips/Monemizadeh20 fatcat:moe2pwvrfrbxnmx6wbj2rzuj5a

Bridging the gap between theory and practice of approximate Bayesian inference

Johan Kwisthout, Iris van Rooij
2013 Cognitive Systems Research  
We will sketch how (parameterized) computational complexity analyses can yield model variants that are tractable and which can serve as the basis of computationally plausible models of cognition.  ...  In this paper, three candidate notions of 'approximation' are discussed, each of which has been suggested in the cognitive science literature.  ...  Until now, the parameterized complexity approach to dealing with intractability of cognitive models has focused on exact computations.  ... 
doi:10.1016/j.cogsys.2012.12.008 fatcat:jauxp3azprc5xgol4c6ba3h2xq

Introducing Non-Linearity into Quantum Generative Models [article]

Kaitlin Gili, Mykolas Sveistrys, Chris Ballance
2022 arXiv   pre-print
error rate than a QCBM with a similar number of tunable parameters.  ...  However, some of the most successful classical generative models, such as those based on neural networks, involve highly non-linear and thus non-reversible dynamics.  ...  Thank you to all of the local people who became a part of the journey.  ... 
arXiv:2205.14506v2 fatcat:6ofsbd4auvbevhe6fkcbsfx2ue

Parameterized exact and approximation algorithms for maximumk-set cover and related satisfiability problems

Édouard Bonnet, Vangelis Th. Paschos, Florian Sikora
2016 RAIRO - Theoretical Informatics and Applications  
This problem is W[2]-hard when parameterized by k, and FPT when parameterized by p. We investigate the parameterized approximability of the problem with respect to parameters k and p.  ...  Given a family of subsets S over a set of elements X and two integers p and k, max k-set cover consists of finding a subfamily T ⊆ S of cardinality at most k, covering at least p elements of X.  ...  The pertinent suggestions and comments of two anonymous referees have greatly improved the quality of this paper.  ... 
doi:10.1051/ita/2016022 fatcat:urg3gnvtobbajnkjijvq57pmru

Constrained signaling for welfare and revenue maximization

Shaddin Dughmi, Nicole Immorlica, Aaron Roth
2013 ACM SIGecom Exchanges  
In this paper, we give algorithms for computing constrained signaling schemes, as well as hardness results, for both of these objectives for a variety of constrained signaling problems.  ...  We consider the problem of an auctioneer who faces the task of selling a good (drawn from a known distribution) to a set of buyers, when the seller does not have the capacity to describe to the buyers  ...  Algorithm 1 computes a 2e(2e−1) (e−1) 2 ≈ 8.17 approximation to the optimal revenue in the cardinality-constrained signaling problem. Note 4.7.  ... 
doi:10.1145/2509013.2509022 fatcat:auj2ved2bvf6pgfwli6llrhyxi

Optimal control of discrete-time switched linear systems via continuous parameterization [article]

Jérémie Kreiss, Laurent Bako, Eric Blanco
2017 arXiv   pre-print
The method of this paper addresses the challenge of the switching law design by introducing auxiliary continuous input variables and then solving a non-smooth block-sparsity inducing optimization problem  ...  State-of-art methods for solving such a control problem suffer in general from a high computational requirement due to the fact that an exponential number of switching sequences must be explored.  ...  Then problem (2) can be written as a constrained optimization problem as follows min u(·),f(·) V x 0 , u(·),f (·) s.t.  ... 
arXiv:1704.06985v1 fatcat:7yxzf7blvvbkbbnl75wtvmzxfq

Developing next generation predictive models: a systems biology approach

Jan F. Van Impe, Dominique Vercammen, Eva Van Derlinden
2011 Procedia Food Science  
Flux balance analysis (FBA) uses an objective function to derive, through optimization over the solution space of this underdetermined linear system, an intracellular flux distribution.  ...  This paper discusses the background of the FBA approach and focuses on important issues with respect to the implementation of FBA analysis when modeling dynamic systems.  ...  As a result of the complexity of the microscopic level models, problems arise mainly due to insufficient calculation power and a shortage of intracellular experimental data.  ... 
doi:10.1016/j.profoo.2011.09.145 fatcat:rkpa3vukmfgejoygqevethbrp4
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