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Parameterized Complexity of Cardinality Constrained Optimization Problems
2007
Computer journal
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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
2006
Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06
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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
2009
Proceedings of the 35th SIGMOD international conference on Management of data - SIGMOD '09
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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
2012
Optimization Methods and Software
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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
2015
The Journal of Artificial Intelligence Research
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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]
2000
Lecture Notes in Computer Science
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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
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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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
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
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
2020
Neural Information Processing Systems
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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
2013
Cognitive Systems Research
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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]
2022
arXiv
pre-print
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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
2016
RAIRO - Theoretical Informatics and Applications
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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
2013
ACM SIGecom Exchanges
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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]
2017
arXiv
pre-print
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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
2011
Procedia Food Science
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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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