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The proposed loop-current order in cuprates cannot give the observed pseudogap and the Fermi-arcs because it preserves translation symmetry. A modification to a periodic arrangement of the four possible orientations of the order parameter…

Strongly Correlated Electrons · Physics 2019-07-03 Chandra M. Varma

Due to their capacity for non-classical light generation, high-efficiency second-order nonlinear parametric processes play an important role in quantum photonic technology, and chip-scale realization of these processes is recognized as the…

A systematic comparison is conducted for pairing properties of finite systems at nonzero temperature as predicted by the exact solutions of the pairing problem embedded in three principal statistical ensembles, as well as the unprojected…

Nuclear Theory · Physics 2009-06-30 N. Quang Hung , N. Dinh Dang

Stochastic convex optimization problems with nonlinear functional constraints are ubiquitous in signal processing applications including constrained least-squares, set-membership adaptive filtering, and trajectory optimization under…

Optimization and Control · Mathematics 2025-12-16 Panchajanya Sanyal , Srujan Teja Thomdapu , Ketan Rajawat

We analyze a sequential quadratic programming algorithm for solving a class of abstract optimization problems. Assuming that the initial point is in an $L^2$ neighborhood of a local solution that satisfies no-gap second-order sufficient…

Optimization and Control · Mathematics 2026-05-19 Eduardo Casas , Mariano Mateos

We use the numerical renormalization group method to investigate the spectral properties of a single-impurity Anderson model with a gap {\delta} across the Fermi level in the conduction-electron spectrum. For any finite {\delta} > 0, at…

Mesoscale and Nanoscale Physics · Physics 2010-06-22 Catalin Pascu Moca , Adrian Roman

Nonlinear model predictive control~(NMPC) generally requires the solution of a non-convex optimization problem at each sampling instant under strict timing constraints, based on a set of differential equations that can often be stiff and/or…

Optimization and Control · Mathematics 2019-03-22 Pedro Hespanhol , Rien Quirynen

The optimal operation of electrical energy systems by solving a security constrained optimal power flow (SCOPF) problem is still a challenging research aspect. Especially, for conventional optimization methods like sequential quadratic…

Systems and Control · Electrical Eng. & Systems 2021-06-03 Marcel Sarstedt , Thomas Leveringhaus , Leonard Kluß , Lutz Hofmann

We apply random matrix theory to derive spectral density of large sample covariance matrices generated by multivariate VMA(q), VAR(q) and VARMA(q1,q2) processes. In particular, we consider a limit where the number of random variables N and…

Statistical Finance · Quantitative Finance 2015-05-18 Zdzisław Burda , Andrzej Jarosz , Maciej A. Nowak , Małgorzata Snarska

The quasiparticle concept is an important tool for the description of many-body systems. We study the quasiparticle properties for dilute Fermi systems with short-ranged, repulsive interactions using effective field theory. We calculate the…

Nuclear Theory · Physics 2009-11-07 L. Platter , H. -W. Hammer , Ulf-G. Meißner

Based on an equations--of--motion approach for time--dependent pair correlations in strongly interacting Fermi liquids, we have developed a theory for describing the excitation spectrum of these systems. Compared to the known ``correlated''…

Other Condensed Matter · Physics 2009-11-11 Helga M. Böhm , Eckhard Krotscheck , Martin Panholzer

Sequential Quadratic Programming (SQP) is a powerful class of algorithms for solving nonlinear optimization problems. Local convergence of SQP algorithms is guaranteed when the Hessian approximation used in each Quadratic Programming…

Optimization and Control · Mathematics 2017-04-12 Tuan T. Nguyen , Mircea Lazar , Hans Butler

In the paper we describe the modification of self-consistent harmonic approximation for quantum S=1 systems. This method has a number of advantages in comparison with usual SCHA. We apply the method to two-dimensional ferromagnets with…

Strongly Correlated Electrons · Physics 2007-05-23 D. V. Spirin

The mean spherical approximation (MSA) can be solved semi-analytically for the Gaussian core model (GCM) and yields - rather surprisingly - exactly the same expressions for the energy and the virial equations. Taking advantage of this…

Soft Condensed Matter · Physics 2016-08-31 Bianca M. Mladek , Gerhard Kahl , Martin Neumann

Relativistic plasma with radiation at thermodynamic equilibrium is ageneral system of interest in astrophysics and high energy physics. We develop a new self-consistent quasi-particle model for such a system to take account of collective…

High Energy Physics - Phenomenology · Physics 2009-11-11 Vishnu M. Bannur

The functional renormalization group (fRG) approach has the property that, in general, the flow equation for the two-particle vertex generates $\mathcal{O}(N^4)$ independent variables, where $N$ is the number of interacting states (e.g.…

Strongly Correlated Electrons · Physics 2014-01-24 Florian Bauer , Jan Heyder , Jan von Delft

The deconfined quantum critical point (DQCP) is an example of phase transitions beyond the Landau symmetry breaking paradigm that attracts wide interest. However, its nature has not been settled after decades of study. In this paper, we…

Strongly Correlated Electrons · Physics 2025-07-18 Zheng Zhou , Liangdong Hu , W. Zhu , Yin-Chen He

The random phase approximation (RPA) and the $GW$ approximation share the same total energy functional but RPA is defined on a restricted domain of Green's functions determined by a local Kohn-Sham (KS) potential. In this work, we perform…

Materials Science · Physics 2025-08-26 Thomas Pitts , Damian Contant , Maria Hellgren

We introduce an equilibrium formulation of the functional renormalization group (fRG) for inhomogeneous systems capable of dealing with spatially finite-ranged interactions. In the general third order truncated form of fRG, the dependence…

Strongly Correlated Electrons · Physics 2017-01-17 Lukas Weidinger , Florian Bauer , Jan von Delft

Quantum principal component analysis (qPCA) is commonly formulated as the extraction of eigenvalues and eigenvectors of a covariance-encoded density operator. Yet in many qPCA settings the practical goal is simpler: projection onto the…

Machine Learning · Statistics 2026-05-08 Sk Mujaffar Hossain , Satadeep Bhattacharjee