Variational methods for contracting projected entangled-pair states
- URL: http://arxiv.org/abs/2110.12726v2
- Date: Tue, 7 Jun 2022 09:17:21 GMT
- Title: Variational methods for contracting projected entangled-pair states
- Authors: Laurens Vanderstraeten, Lander Burgelman, Boris Ponsioen, Maarten Van
Damme, Bram Vanhecke, Philippe Corboz, Jutho Haegeman and Frank Verstraete
- Abstract summary: In this paper, we identify a subclass of PEPS for which we can reformulate the contraction as a variational problem that is algorithm independent.
We use this variational feature to assess and compare the accuracy of CTMRG and VUMPS contractions.
We devise a new variational contraction scheme, which we can extend to compute general N-point correlation functions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The norms or expectation values of infinite projected entangled-pair states
(PEPS) cannot be computed exactly, and approximation algorithms have to be
applied. In the last years, many efficient algorithms have been devised -- the
corner transfer matrix renormalization group (CTMRG) and variational uniform
matrix product state (VUMPS) algorithm are the most common -- but it remains
unclear whether they always lead to the same results. In this paper, we
identify a subclass of PEPS for which we can reformulate the contraction as a
variational problem that is algorithm independent. We use this variational
feature to assess and compare the accuracy of CTMRG and VUMPS contractions.
Moreover, we devise a new variational contraction scheme, which we can extend
to compute general N-point correlation functions.
Related papers
- Improving Anomaly Detection in Industrial Time Series: The Role of Segmentation and Heterogeneous Ensemble [36.94429692322632]
We show how the integration of segmentation techniques, combined with a heterogeneous ensemble, can enhance anomaly detection in an industrial production context.<n>We improve the AUC-ROC metric from 0.8599 (achieved with a PCA and LSTM ensemble) to 0.9760 (achieved with Random Forest and XGBoost)<n>In our future work, we intend to assess the benefit of introducing weighted features derived from the study of change points, combined with segmentation and the use of heterogeneous ensembles, to further optimize model performance in early anomaly detection.
arXiv Detail & Related papers (2025-10-10T07:24:47Z) - Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models [3.6034001987137767]
We show that scale-invariance inherent to low-rank approximation models causes an implicit regularization with both unexpected beneficial and detrimental effects.
We derive a generic Majorization Minimization algorithm that handles many regularized nonnegative low-rank approximations.
We showcase our contributions on sparse Nonnegative Matrix Factorization, ridge-regularized Canonical Polyadic decomposition and sparse Nonnegative Tucker Decomposition.
arXiv Detail & Related papers (2024-03-27T12:49:14Z) - Convergence of Expectation-Maximization Algorithm with Mixed-Integer Optimization [5.319361976450982]
This paper introduces a set of conditions that ensure the convergence of a specific class of EM algorithms.
Our results offer a new analysis technique for iterative algorithms that solve mixed-integer non-linear optimization problems.
arXiv Detail & Related papers (2024-01-31T11:42:46Z) - A unified consensus-based parallel ADMM algorithm for high-dimensional
regression with combined regularizations [3.280169909938912]
parallel alternating multipliers (ADMM) is widely recognized for its effectiveness in handling large-scale distributed datasets.
The proposed algorithms serve to demonstrate the reliability, stability, and scalability of a financial example.
arXiv Detail & Related papers (2023-11-21T03:30:38Z) - From Optimization to Control: Quasi Policy Iteration [3.4376560669160394]
We introduce a novel control algorithm coined as quasi-policy iteration (QPI)
QPI is based on a novel approximation of the "Hessian" matrix in the policy iteration algorithm by exploiting two linear structural constraints specific to MDPs.
It exhibits an empirical convergence behavior similar to policy iteration with a very low sensitivity to the discount factor.
arXiv Detail & Related papers (2023-11-18T21:00:14Z) - On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality [5.35599092568615]
This paper considers solving distributed optimization problems in peer-to-peer multi-agent networks.
By using the proportional-integral (PI) control strategy, various algorithms with fixed stepsize have been developed.
arXiv Detail & Related papers (2023-09-30T15:54:52Z) - Fractal Structure and Generalization Properties of Stochastic
Optimization Algorithms [71.62575565990502]
We prove that the generalization error of an optimization algorithm can be bounded on the complexity' of the fractal structure that underlies its generalization measure.
We further specialize our results to specific problems (e.g., linear/logistic regression, one hidden/layered neural networks) and algorithms.
arXiv Detail & Related papers (2021-06-09T08:05:36Z) - Benign Overfitting of Constant-Stepsize SGD for Linear Regression [122.70478935214128]
inductive biases are central in preventing overfitting empirically.
This work considers this issue in arguably the most basic setting: constant-stepsize SGD for linear regression.
We reflect on a number of notable differences between the algorithmic regularization afforded by (unregularized) SGD in comparison to ordinary least squares.
arXiv Detail & Related papers (2021-03-23T17:15:53Z) - Joint Network Topology Inference via Structured Fusion Regularization [70.30364652829164]
Joint network topology inference represents a canonical problem of learning multiple graph Laplacian matrices from heterogeneous graph signals.
We propose a general graph estimator based on a novel structured fusion regularization.
We show that the proposed graph estimator enjoys both high computational efficiency and rigorous theoretical guarantee.
arXiv Detail & Related papers (2021-03-05T04:42:32Z) - Accelerated Message Passing for Entropy-Regularized MAP Inference [89.15658822319928]
Maximum a posteriori (MAP) inference in discrete-valued random fields is a fundamental problem in machine learning.
Due to the difficulty of this problem, linear programming (LP) relaxations are commonly used to derive specialized message passing algorithms.
We present randomized methods for accelerating these algorithms by leveraging techniques that underlie classical accelerated gradient.
arXiv Detail & Related papers (2020-07-01T18:43:32Z) - Slice Sampling for General Completely Random Measures [74.24975039689893]
We present a novel Markov chain Monte Carlo algorithm for posterior inference that adaptively sets the truncation level using auxiliary slice variables.
The efficacy of the proposed algorithm is evaluated on several popular nonparametric models.
arXiv Detail & Related papers (2020-06-24T17:53:53Z) - Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
Adaptivity is an important yet under-studied property in modern optimization theory.
Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
arXiv Detail & Related papers (2020-02-13T05:42:27Z) - Sparse Orthogonal Variational Inference for Gaussian Processes [34.476453597078894]
We introduce a new interpretation of sparse variational approximations for Gaussian processes using inducing points.
We show that this formulation recovers existing approximations and at the same time allows to obtain tighter lower bounds on the marginal likelihood and new variational inference algorithms.
arXiv Detail & Related papers (2019-10-23T15:01:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.