TY - CONF
AB - A class of valued constraint satisfaction problems (VCSPs) is characterised by a valued constraint language, a fixed set of cost functions on a finite domain. An instance of the problem is specified by a sum of cost functions from the language with the goal to minimise the sum. We study which classes of finite-valued languages can be solved exactly by the basic linear programming relaxation (BLP). Thapper and Živný showed [20] that if BLP solves the language then the language admits a binary commutative fractional polymorphism. We prove that the converse is also true. This leads to a necessary and a sufficient condition which can be checked in polynomial time for a given language. In contrast, the previous necessary and sufficient condition due to [20] involved infinitely many inequalities. More recently, Thapper and Živný [21] showed (using, in particular, a technique introduced in this paper) that core languages that do not satisfy our condition are NP-hard. Taken together, these results imply that a finite-valued language can either be solved using Linear Programming or is NP-hard.
AU - Kolmogorov, Vladimir
ID - 2518
IS - 1
TI - The power of linear programming for finite-valued CSPs: A constructive characterization
VL - 7965
ER -
TY - CONF
AB - We propose a probabilistic model to infer supervised latent variables in
the Hamming space from observed data. Our model allows simultaneous
inference of the number of binary latent variables, and their values. The
latent variables preserve neighbourhood structure of the data in a sense
that objects in the same semantic concept have similar latent values, and
objects in different concepts have dissimilar latent values. We formulate
the supervised infinite latent variable problem based on an intuitive
principle of pulling objects together if they are of the same type, and
pushing them apart if they are not. We then combine this principle with a
flexible Indian Buffet Process prior on the latent variables. We show that
the inferred supervised latent variables can be directly used to perform a
nearest neighbour search for the purpose of retrieval. We introduce a new
application of dynamically extending hash codes, and show how to
effectively couple the structure of the hash codes with continuously
growing structure of the neighbourhood preserving infinite latent feature
space.
AU - Quadrianto, Novi
AU - Sharmanska, Viktoriia
AU - Knowles, David
AU - Ghahramani, Zoubin
ID - 2520
SN - 9780974903996
T2 - Proceedings of the 29th conference uncertainty in Artificial Intelligence
TI - The supervised IBP: Neighbourhood preserving infinite latent feature models
ER -
TY - JOUR
AB - We consider non-interacting particles subject to a fixed external potential V and a self-generated magnetic field B. The total energy includes the field energy β∫B2 and we minimize over all particle states and magnetic fields. In the case of spin-1/2 particles this minimization leads to the coupled Maxwell-Pauli system. The parameter β tunes the coupling strength between the field and the particles and it effectively determines the strength of the field. We investigate the stability and the semiclassical asymptotics, h→0, of the total ground state energy E(β,h,V). The relevant parameter measuring the field strength in the semiclassical limit is κ=βh. We are not able to give the exact leading order semiclassical asymptotics uniformly in κ or even for fixed κ. We do however give upper and lower bounds on E with almost matching dependence on κ. In the simultaneous limit h→0 and κ→∞ we show that the standard non-magnetic Weyl asymptotics holds. The same result also holds for the spinless case, i.e. where the Pauli operator is replaced by the Schrödinger operator.
AU - Erdös, László
AU - Fournais, Søren
AU - Solovej, Jan
ID - 2698
IS - 6
JF - Journal of the European Mathematical Society
TI - Stability and semiclassics in self-generated fields
VL - 15
ER -
TY - CONF
AB - Even though both population and quantitative genetics, and evolutionary computation, deal with the same questions, they have developed largely independently of each other. I review key results from each field, emphasising those that apply independently of the (usually unknown) relation between genotype and phenotype. The infinitesimal model provides a simple framework for predicting the response of complex traits to selection, which in biology has proved remarkably successful. This allows one to choose the schedule of population sizes and selection intensities that will maximise the response to selection, given that the total number of individuals realised, C = ∑t Nt, is constrained. This argument shows that for an additive trait (i.e., determined by the sum of effects of the genes), the optimum population size and the maximum possible response (i.e., the total change in trait mean) are both proportional to √C.
AU - Barton, Nicholas H
AU - Paixao, Tiago
ID - 2718
T2 - Proceedings of the 15th annual conference on Genetic and evolutionary computation
TI - Can quantitative and population genetics help us understand evolutionary computation?
ER -
TY - CONF
AB - Prediction of the evolutionary process is a long standing problem both in the theory of evolutionary biology and evolutionary computation (EC). It has long been realized that heritable variation is crucial to both the response to selection and the success of genetic algorithms. However, not all variation contributes in the same way to the response. Quantitative genetics has developed a large body of work trying to estimate and understand how different components of the variance in fitness in the population contribute to the response to selection. We illustrate how to apply some concepts of quantitative genetics to the analysis of genetic algorithms. In particular, we derive estimates for the short term prediction of the response to selection and we use variance decomposition to gain insight on local aspects of the landscape. Finally, we propose a new population based genetic algorithm that uses these methods to improve its operation.
AU - Paixao, Tiago
AU - Barton, Nicholas H
ID - 2719
T2 - Proceedings of the 15th annual conference on Genetic and evolutionary computation
TI - A variance decomposition approach to the analysis of genetic algorithms
ER -