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Stochastic First-Order Methods with Random Constraint Projection

Mengdi Wang, Dimitri P. Bertsekas
2016 SIAM Journal on Optimization  
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

Nicholas V. Hammond
1977 Behavior Research Methods & Instrumentation  
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

Gabriel Istrate, Stefan Boettcher, Allon G. Percus
2005 Annals of Mathematics and Artificial Intelligence  
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]

Gabriel Istrate, Stefan Boettcher, Allon G. Percus
2005 arXiv   pre-print
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

Christopher W Tyler
2004 Vision Research  
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

Muhammad Rezaul Karim, Malek Mouhoub
2014 2014 IEEE Congress on Evolutionary Computation (CEC)  
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]

Nan Liu, Sennur Ulukus
2006 arXiv   pre-print
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]

Miguel A. Salido, Federico Barber
2004 Lecture Notes in Computer Science  
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]

Thanasis Balafoutis, Kostas Stergiou
2010 arXiv   pre-print
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]

Steven D. Prestwich, Roberto Rossi, S. Armagan Tarim
2015 Lecture Notes in Computer Science  
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

Nan Liu, Sennur Ulukus
2006 2006 IEEE International Symposium on Information Theory  
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

Maaarten van Casteren, Matthew H. Davis
2006 Behavior Research Methods  
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

Pieter Tijtgat, Simon J. Bennett, Geert J. P. Savelsbergh, Dirk De Clercq, Matthieu Lenoir
2009 Experimental Brain Research  
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]

Konstantin Makarychev
2013 arXiv   pre-print
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

Dong Liu, Qinpeng Li, Yan Ru, Jun Zhang
2020 IEEE Access  
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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