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Stochastic First-Order Methods with Random Constraint Projection
2016
SIAM Journal on Optimization
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We propose an algorithmic framework for stochastic first-order methods using random projection/proximal updates and random constraint updates, which contain as special cases several known algorithms as ...
We also consider a number of typical sampling processes for generating stochastic first-order information and random constraints, which are common in data-intensive applications, online learning, and simulation ...
Stochastic first-order method with random constraint projection. ...
doi:10.1137/130931278
fatcat:kd22rn7xlndnvdtr6dmqiv4sfy
A program to generate random sequences of multidimensional stimuli with constraints on first-order and second-order contingencies
1977
Behavior Research Methods & Instrumentation
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The program described here allows the user to generate sequences of codes with specified numbers of instances of each type of code, and with specified constraints on second-order contingencies (arrangements ...
The program performs random shuffles of the stimuli and compares the frequencies of the second-order configurations with the requested frequencies until an acceptable sequence is found. ...
The program described here allows the user to generate sequences of codes with specified numbers of instances of each type of code, and with specified constraints on second-order contingencies (arrangements ...
doi:10.3758/bf03214003
fatcat:k7a6nma3frg4tgm3ltzcvbf46i
Spines of random constraint satisfaction problems: definition and connection with computational complexity
2005
Annals of Mathematics and Artificial Intelligence
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Specifically, we extend the definition of the spine order parameter of Bollobás et al. [7] to random constraint satisfaction problems, rigorously showing that for such problems a discontinuity of the spine ...
is associated with a 2 Ω(n) resolution complexity (and thus a 2 Ω(n) complexity of DPLL algorithms) on random instances. ...
Choose a random constraint template from C and apply it to the ordered list of
literals in o e . ...
doi:10.1007/s10472-005-7033-2
fatcat:u43fbvbrajczvmn5wmb4tkwwsm
Spines of Random Constraint Satisfaction Problems: Definition and Connection with Computational Complexity
[article]
2005
arXiv
pre-print
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Specifically, we extend the definition of the spine order parameter of Bollobas et al. to random constraint satisfaction problems, rigorously showing that for such problems a discontinuity of the spine ...
is associated with a 2^Ω(n) resolution complexity (and thus a 2^Ω(n) complexity of DPLL algorithms) on random instances. ...
Choose a random constraint template from C and apply it to the ordered list of
literals in o e . ...
arXiv:cs/0503082v1
fatcat:wfqggbv43napzheyyiiwwrdmnu
Beyond fourth-order texture discrimination: generation of extreme-order and statistically-balanced textures
2004
Vision Research
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A 3 (1986) 868] offered a variety of paradigms suggesting that fourth order might be the limit on human texture processing. ...
Each substantially degrades the visibility of the fourth-order textures, revealing that the fourth-order information is conveyed largely by local rather than long-range perturbations from random statistics ...
Now the fourth-order constraint is on sets of irregular quadrilateral points aligned in neither rows nor columns. ...
doi:10.1016/j.visres.2004.03.032
pmid:15183686
fatcat:y6jtx6qh7vfgldciigsoe2srzm
Coevolutionary genetic algorithm for variable ordering in CSPs
2014
2014 IEEE Congress on Evolutionary Computation (CEC)
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2013 pre-print arXiv:1312.6996v1 fatcat:h7oxugzcurai5j32kwgftd5xom
We have conducted experiments on several random, quasi-random and patterned instances to measure the efficiency of the proposed approach. ...
Many powerful variable ordering heuristics weigh the constraints first and then utilize the weights for selecting good order of the variables. ...
VARIABLE ORDERING BASED ON CONSTRAINT WEIGHTS In this paper, we follow two steps to come up with a good variable ordering. ...
doi:10.1109/cec.2014.6900262
dblp:conf/cec/KarimM14
fatcat:35wvsjlxy5at3mxqe65p5falme
Optimal Distortion-Power Tradeoffs in Gaussian Sensor Networks
[article]
2006
arXiv
pre-print
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Thus, for these random processes, under these sum power constraints, we determine the achievability scheme that is order-optimal, and express the minimum achievable expected distortion as a function of ...
In the case where the random process satisfies some general conditions, we evaluate the lower and upper bounds explicitly and show that they are of the same order for a wide range of sum power constraints ...
Then, we focus on the case where the Gaussian random process also satisfies some general conditions, evaluate the lower and upper bounds explicitly, and show that they are of the same order, for a wide ...
arXiv:cs/0605116v1
fatcat:eewx752xtzcz7b2j6hqx7pexwu
Constrainedness and Redundancy by Constraint Ordering
[chapter]
2004
Lecture Notes in Computer Science
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To achieve this parameter, a simple random or systematic sampling is carried out to compute the tightnesses of each constraint. ...
New heuristics are developed to classify the constraints from the tightest constraint to the loosest constraint and to remove redundant constraints in constraint satisfaction problems. ...
For future work, we are working on integrating constraint ordering with variable ordering in centralized and distributed CSPs. ...
doi:10.1007/978-3-540-30498-2_13
fatcat:vegtunfmvzhz7a43llxzxcw5j4
Evaluating and Improving Modern Variable and Revision Ordering Strategies in CSPs
[article]
2010
arXiv
pre-print
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Based on this, we then derive new revision ordering heuristics that exploit recorded failures to efficiently order the propagation list when arc consistency is maintained during search. ...
Results demonstrate that heuristics based on failures are in general more efficient. ...
In Section 5 after a short summary on the existing revision ordering heuristics for constraint propagation, we propose a set of new revision ordering heuristics based on constraint weights. ...
arXiv:1008.0659v2
fatcat:3pp52nmikveszbv7o7qmmy3hiy
Randomness as a Constraint
[chapter]
2015
Lecture Notes in Computer Science
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We propose a constraint-based approach to finding pseudo-random solutions, inspired by the Kolmogorov complexity definition of randomness and by data compression methods. ...
Randomised search is not a good general approach because problem constraints and objective functions may lead to solutions that are far from random. ...
We order the v i in strictly ascending order using a global constraint ordered(v). ...
doi:10.1007/978-3-319-23219-5_25
fatcat:al7cw45o5ng3zcjpzy5a66na2i
Optimal Distortion-Power Tradeoffs in Gaussian Sensor Networks
2006
2006 IEEE International Symposium on Information Theory
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Thus, for these random processes, under these sum power constraints, we determine the achievability scheme that is order-optimal, and express the minimum achievable expected distortion as a function of ...
In the case where the random process satisfies some general conditions, we evaluate the lower and upper bounds explicitly and show that they are of the same order for a wide range of sum power constraints ...
Then, we focus on the case where the Gaussian random process also satisfies some general conditions, evaluate the lower and upper bounds explicitly, and show that they are of the same order, for a wide ...
doi:10.1109/isit.2006.262125
dblp:conf/isit/LiuU06
fatcat:gd7l2j66fzegfnfjxayngimzlq
Mix, a program for pseudorandomization
2006
Behavior Research Methods
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This constraint may be difficult to achieve if random list orders are generated. ...
ordered experimental lists in which a number of different constraints on trial ordering are simultaneously satisfied. ...
doi:10.3758/bf03193889
pmid:17393828
fatcat:g5vvhyw7urdhtgvwk7ontxp3n4
Advance knowledge effects on kinematics of one-handed catching
2009
Experimental Brain Research
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In both the blocked-order and random-order conditions, catching performance (number of catches, touches and misses) decreased with increasing temporal constraints. ...
Based on expectancy of previous trials, movement kinematics was scaled to ball speed in the blocked-order condition whereas in the random-order condition, participants exhibited a more default initial ...
By presenting balls to be caught at one of four diVerent ball speeds in either blocked-order or random-order, we aimed to determine if participants' certainty of expectation regarding the temporal constraints ...
doi:10.1007/s00221-009-2102-0
pmid:19949776
fatcat:rgurje2cljfkfeaz3ia3skqph4
Local Search is Better than Random Assignment for Bounded Occurrence Ordering k-CSPs
[article]
2013
arXiv
pre-print
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solution; and a(B,k)=Omega_k(B^-(k+O(1)) is a parameter depending only on "k" (the arity of the CSP) and "B" (the maximum number of times each variable is used in constraints). ...
We prove that the Bounded Occurrence Ordering k-CSP Problem is not approximation resistant. ...
Let f (x) be the expected total value of constraints in C u on a random ordering π sampled from the distribution U x : f (x) = E π∼Ux C∈Cu C(π) . ...
arXiv:1210.1890v2
fatcat:if5gfbunwjemrowa76qwljcqse
The network representation learning algorithm based on semi-supervised random walk
2020
IEEE Access
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The experimental results on eight real networks show that comparing with the popular network embedding methods, the node representation vectors integrating pairwise constraints into the random walk process ...
Specifically, a semi-supervised random walk sampling method which effectively integrates the pairwise constraints is proposed. ...
From the results of link prediction task on eight datasets, the effect of SSDW model based on semi-supervised random walk on link prediction task is better than DeepWalk model based on unsupervised random ...
doi:10.1109/access.2020.3044367
fatcat:6xajmaithvff7fqkzntrx34vua
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