Multivariate convergence-targeted operator for the genetic algorithm

Optimization of complex particle transport simulation packages could be managed using genetic algorithms as a tuning instrument for learning statistics and behavior of multi-objective optimisation functions. Combination of genetic algorithm and unsupervised machine learning could significantly increase convergence of algorithm to true Pareto Front (PF). We tried to apply specific multivariate analysis operator that can be used in case of expensive fitness function evaluations, in order to speed-up the convergence of the "black-box" optimization problem. The results delivered in the article shows that current approach could be used for any type of genetic algorithm and deployed as a separate genetic operator.Cкладні пакети моделювання транспорту частинок можна оптимізувати за допомогою генетичних алгоритмів, що дає змогу застосовувати для таких задач підходи статистичного навчання та методи оптимізації декількох цільових функцій. Поєднання генетичного алгоритму та неконтрольованого машинного навчання значно підвищує збіжність алгоритму до істинного парето-фронту. У межах багатофакторного аналізу запропоновано додатковий оператор, який може бути застосований для задач оптимізації багатоцільових функцій, що потребують великого обсягу ресурсів та часу, зокрема для пришвидшення збіжності задачі оптимізації "чорного ящика". Отримані результати показують, що запропонований підхід можна використовувати для генетичного алгоритму будь-якого типу, а додатковий оператор розглядати як окремий генетичний оператор.Сложные пакеты моделирования транспорта частиц можно оптимизировать с помощью генетических алгоритмов, что позволяет применять для таких задач подходы статистического обучения и методы оптимизации нескольких целевых функций. Сочетание генетического алгоритма и неконтролируемого машинного обучения может значительно повышает сходимость алгоритма к истинному парето-фронта. В рамках многофакторного анализа предложен дополнительный оператор, который может быть применен для задач оптимизации многоцелевых функций, требующих большого объема ресурсов и времени, в частности для ускорения сходимости задачи оптимизации "черного ящика". Полученные результаты показывают, что предложенный подход можно использовать для генетического алгоритма любого типа, а дополнительный оператор рассматривать как отдельный генетический оператор

Form-factors in the Baxter-Bazhanov-Stroganov model I: Norms and matrix elements

We continue our investigation of the Z_N-Baxter-Bazhanov-Stroganov model using the method of separation of variables [nlin/0603028]. In this paper we calculate the norms and matrix elements of a local Z_N-spin operator between eigenvectors of the auxiliary problem. For the norm the multiple sums over the intermediate states are performed explicitly. In the case N=2 we solve the Baxter equation and obtain form-factors of the spin operator of the periodic Ising model on a finite lattice.Comment: 24 page

Spin operator matrix elements in the quantum Ising chain: fermion approach

Using some modification of the standard fermion technique we derive factorized formula for spin operator matrix elements (form-factors) between general eigenstates of the Hamiltonian of quantum Ising chain in a transverse field of finite length. The derivation is based on the approach recently used to derive factorized formula for Z_N-spin operator matrix elements between ground eigenstates of the Hamiltonian of the Z_N-symmetric superintegrable chiral Potts quantum chain. The obtained factorized formulas for the matrix elements of Ising chain coincide with the corresponding expressions obtained by the Separation of Variables Method.Comment: 19 page

Transfer matrix eigenvectors of the Baxter-Bazhanov-Stroganov τ2\tau_2-model for N=2

We find a representation of the row-to-row transfer matrix of the Baxter-Bazhanov-Stroganov $\tau_2$-model for N=2 in terms of an integral over two commuting sets of grassmann variables. Using this representation, we explicitly calculate transfer matrix eigenvectors and normalize them. It is also shown how form factors of the model can be expressed in terms of determinants and inverses of certain Toeplitz matrices.Comment: 23 page

Eigenvectors of Baxter-Bazhanov-Stroganov \tau^{(2)}(t_q) model with fixed-spin boundary conditions

The aim of this contribution is to give the explicit formulas for the eigenvectors of the transfer-matrix of Baxter-Bazhanov-Stroganov (BBS) model (N-state spin model) with fixed-spin boundary conditions. These formulas are obtained by a limiting procedure from the formulas for the eigenvectors of periodic BBS model. The latter formulas were derived in the framework of the Sklyanin's method of separation of variables. In the case of fixed-spin boundaries the corresponding T-Q Baxter equations for the functions of separated variables are solved explicitly. As a particular case we obtain the eigenvectors of the Hamiltonian of Ising-like Z_N quantum chain model.Comment: 14 pages, paper submitted to Proceedings of the International Workshop "Classical and Quantum Integrable Systems" (Dubna, January, 2007

(l,q)(l,q)-Deformed Grassmann Field and the Two-dimensional Ising Model

In this paper we construct the exact representation of the Ising partition function in the form of the $SL_q(2,R)$-invariant functional integral for the lattice free $(l,q)$-fermion field theory ($l=q=-1$). It is shown that the $(l,q)$-fermionization allows one to re-express the partition function of the eight-vertex model in external field through functional integral with four-fermion interaction. To construct these representations, we define a lattice $(l,q,s)$-deformed Grassmann bispinor field and extend the Berezin integration rules to this field. At $l=q=-1, s=1$ we obtain the lattice $(l,q)$-fermion field which allows us to fermionize the two-dimensional Ising model. We show that the Gaussian integral over $(q,s)$-Grassmann variables is expressed through the $(q,s)$-deformed Pfaffian which is equal to square root of the determinant of some matrix at $q=\pm 1, s=\pm 1$.Comment: 24 pages, LaTeX; minor change

GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP

Full detector simulation was among the largest CPU consumer in all CERN experiment software stacks for the first two runs of the Large Hadron Collider (LHC). In the early 2010's, the projections were that simulation demands would scale linearly with luminosity increase, compensated only partially by an increase of computing resources. The extension of fast simulation approaches to more use cases, covering a larger fraction of the simulation budget, is only part of the solution due to intrinsic precision limitations. The remainder corresponds to speeding-up the simulation software by several factors, which is out of reach using simple optimizations on the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport codes in order to make them benefit from fine-grained parallelism features such as vectorization, but also from increased code and data locality. This paper presents extensively the results and achievements of this R&D, as well as the conclusions and lessons learnt from the beta prototype.Comment: 34 pages, 26 figures, 24 table

Form-factors in the Baxter-Bazhanov-Stroganov model II: Ising model on the finite lattice

We continue our investigation of the Baxter-Bazhanov-Stroganov or \tau^{(2)}-model using the method of separation of variables [nlin/0603028,arXiv:0708.4342]. In this paper we derive for the first time the factorized formula for form-factors of the Ising model on a finite lattice conjectured previously by A.Bugrij and O.Lisovyy in [arXiv:0708.3625,arXiv:0708.3643]. We also find the matrix elements of the spin operator for the finite quantum Ising chain in a transverse field.Comment: 25 pages; sections 8 and A.2 are extended, 2 related references are adde

Factorized finite-size Ising model spin matrix elements from Separation of Variables

Using the Sklyanin-Kharchev-Lebedev method of Separation of Variables adapted to the cyclic Baxter--Bazhanov--Stroganov or $\tau^{(2)}$-model, we derive factorized formulae for general finite-size Ising model spin matrix elements, proving a recent conjecture by Bugrij and Lisovyy

Integral equations and large-time asymptotics for finite-temperature Ising chain correlation functions

This work concerns the dynamical two-point spin correlation functions of the transverse Ising quantum chain at finite (non-zero) temperature, in the universal region near the quantum critical point. They are correlation functions of twist fields in the massive Majorana fermion quantum field theory. At finite temperature, these are known to satisfy a set of integrable partial differential equations, including the sinh-Gordon equation. We apply the classical inverse scattering method to study them, finding that the ``initial scattering data'' corresponding to the correlation functions are simply related to the one-particle finite-temperature form factors calculated recently by one of the authors. The set of linear integral equations (Gelfand-Levitan-Marchenko equations) associated to the inverse scattering problem then gives, in principle, the two-point functions at all space and time separations, and all temperatures. From them, we evaluate the large-time asymptotic expansion ``near the light cone'', in the region where the difference between the space and time separations is of the order of the correlation length