Showing papers on "Constant (mathematics) published in 2021"

On the constant scalar curvature Kähler metrics (I)—A priori estimates

Abstract: In this paper, we derive apriori estimates for constant scalar curvature K\\\"ahler metrics on a compact K\\\"ahler manifold. We show that higher order derivatives can be estimated in terms of a $C^0$ bound for the K\\\"ahler potential. We also discuss some local versions of these estimates which can be of independent interest.

On the constant scalar curvature Kähler metrics (II)—Existence results

Abstract: In this paper, we generalize our apriori estimates on cscK(constant scalar curvature K\\\"ahler) metric equation to more general scalar curvature type equations (e.g., twisted cscK metric equation). As applications, under the assumption that the automorphism group is discrete, we prove the celebrated Donaldson's conjecture that the non-existence of cscK metric is equivalent to the existence of a destabilized geodesic ray where the $K$-energy is non-increasing. Moreover, we prove that the properness of $K$-energy in terms of $L^1$ geodesic distance $d_1$ in the space of K\\\"ahler potentials implies the existence of cscK metric. Finally, we prove that weak minimizers of the $K$-energy in $(\\mathcal{E}^1, d_1)$ are smooth.

On the development of variable-order fractional hyperchaotic economic system with a nonlinear model predictive controller

Abstract: Mathematical modelling plays an indispensable role in our understanding of systems and phenomena. However, most mathematical models formulated for systems either have an integer order derivate or posses constant fractional-order derivative. Hence, their performance significantly deteriorates in some conditions. For the first time in the current paper, we develop a model of an economic system with variable-order fractional derivatives. Our underlying assumption is that the values of fractional derivatives are time-varying functions instead of constant parameters. The effects of variable-order time derivative into the economic system is studied. The dependency of the system's behaviour on the value of the fractional-order derivative is investigated. Afterwards, a nonlinear model predictive controller (NMPC) for hyperchaotic control of the system is suggested. The necessary optimality and sufficient conditions for solving the nonlinear optimal control problem (NOCP) of the NMPC in the form of fractional calculus with variable-order which is formulated as a two-point boundary value problem (TPBVP) are derived. Since the proposed methodology is a robust controller, the efficiency of the proposed controller in the presence of external bounded disturbances is examined. Simulation results show that not only does the presented control approach suppresses the related chaotic behaviour and stabilizes the close-loop system, but it also rejects the external bounded disturbances.

Constant sign and nodal solutions for superlinear double phase problems

Abstract: Abstract We consider a double phase problems with unbalanced growth and a superlinear reaction, which need not satisfy the Ambrosetti–Rabinowitz condition. Using variational tools and the Nehari method, we show that the Dirichlet problem has at least three nontrivial solutions, a positive solution, a negative solution and a nodal solution. The nodal solution has exactly two nodal domains.

“SPOCU”: scaled polynomial constant unit activation function

Abstract: We address the following problem: given a set of complex images or a large database, the numerical and computational complexity and quality of approximation for neural network may drastically differ from one activation function to another. A general novel methodology, scaled polynomial constant unit activation function “SPOCU,” is introduced and shown to work satisfactorily on a variety of problems. Moreover, we show that SPOCU can overcome already introduced activation functions with good properties, e.g., SELU and ReLU, on generic problems. In order to explain the good properties of SPOCU, we provide several theoretical and practical motivations, including tissue growth model and memristive cellular nonlinear networks. We also provide estimation strategy for SPOCU parameters and its relation to generation of random type of Sierpinski carpet, related to the [pppq] model. One of the attractive properties of SPOCU is its genuine normalization of the output of layers. We illustrate SPOCU methodology on cancer discrimination, including mammary and prostate cancer and data from Wisconsin Diagnostic Breast Cancer dataset. Moreover, we compared SPOCU with SELU and ReLU on large dataset MNIST, which justifies usefulness of SPOCU by its very good performance.

High-order fully actuated system approaches: Part II. Generalized strict-feedback systems

Abstract: An advantage of a high-order fully-actuated (HOFA) system is that there exists a controller such that a constant linear closed-loop system with an arbitrarily assignable eigenstructure can be obtai...

Adsorption: fundamental aspects and applications of adsorption for effluent treatment

Abstract: In this chapter, it will be given the definitions of adsorption, chemisorption, and physisorption. Subsequently, it is presented the equilibrium of adsorption with several isotherm models using nonlinearized plots. The obtention of each equilibrium constant in L/mg is also shown. Each of these constant needs to be converted into L/mol before calculating the thermodynamic equilibrium constant. Once obtained, the thermodynamic equilibrium-constant is used for being applied in linearized and nonlinear van´t Hoff equation. Statistical evaluation of the models using R², R²adj, and Bayesian information criterion values (BIC). The kinetic models (based on chemical reactions, empiric kinetic models, and diffusive models) were explored. Moreover, the comparison of linear and nonlinear pseudo-first-order and pseudo-second-order kinetics model and Langmuir isotherm model was performed. The results showed that it is always advisable to use nonlinear fitting for isotherms and mainly for kinetics of adsorption.

Qualitative analysis of caputo fractional integro-differential equations with constant delays

Abstract: In this paper, a nonlinear Volterra integro-differential equation with Caputo fractional derivative, multiple kernels, and multiple constant delays is considered. The aim of this paper is to investigate qualitative properties of solutions of this equation such as uniform stability, asymptotic stability, and Mittag-Leffler stability of the zero solution as well as boundedness of nonzero solutions. Here, we prove four new theorems on the mentioned properties of the solutions of the considered fractional integro-differential equation. The technique used in the proofs of these theorems includes defining an appropriate Lyapunov function and applying the Lyapunov–Razumikhin method. To illustrate the obtained results, two examples are provided, one of them related to an RLC circuit, to illustrate and show applications of the given results. The obtained results are new, original, and they can be useful for applied researchers in sciences and engineering.

Extreme Points and Majorization: Economic Applications

Abstract: We characterize the set of extreme points of monotonic functions that are either majorized by a given function f or themselves majorize f and show that these extreme points play a crucial role in many economic design problems. Our main results show that each extreme point is uniquely characterized by a countable collection of intervals. Outside these intervals the extreme point equals the original function f and inside the function is constant. Further consistency conditions need to be satisfied pinning down the value of an extreme point in each interval where it is constant. We apply these insights to a varied set of economic problems: equivalence and optimality of mechanisms for auctions and (matching) contests, Bayesian persuasion, optimal delegation, and decision making under uncertainty.

Prescribed-time stabilization of a class of nonlinear systems by linear time-varying feedback

Abstract: This note studies the problem of prescribed-time global stabilization of a class of nonlinear systems, where the nonlinear functions are unknown but satisfy a linear growth condition. By using solutions to a class of parametric Lyapunov equations containing a time-varying parameter which goes to infinity as the time approaches to the prescribed convergence time, linear time-varying feedback is designed explicitly to solve the considered problem, with the help of a Lyapunov-like function. It is shown moreover that the control signal is uniformly bounded by a constant depending on the initial condition. Both linear state feedback and linear observer-based output feedback are considered. The effectiveness of the proposed approach is illustrated by a numerical example borrowed from the literature.

Evolution of initial cell-to-cell variations during a three-year production cycle

Abstract: Consistent quality of the battery system in battery electric vehicles (BEVs) is highly dependent on constant quality of the supplied individual cells. Especially for promising, relatively novel material combinations, cell-to-cell parameter variations may vary over years, since cell manufacturers might have not yet found the ideal composite to produce cells with high capacity and cycle stability. This study investigates the development of cells’ capacity, internal resistance and energy density over a time span of nearly three years for three different batches of the same cell. The cell under investigation is commercially available and offers a promising material combination of silicon-graphite and nickel-rich NMC. Differential voltage and differential capacity analysis are used to explain possible reasons for cell-to-cell variations. As a result, we found significant differences in cell-to-cell variations between the batches. For BEV manufacturers, this means in particular that they should consider how they can counter the influence of these cell-to-cell variations through an operating strategy in order to protect themselves in the long term against regress claims by customers.

LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence

Abstract: We present a novel theoretical framework for computing large, adaptive learning rates. Our framework makes minimal assumptions on the activations used and exploits the functional properties of the loss function. Specifically, we show that the inverse of the Lipschitz constant of the loss function is an ideal learning rate. We analytically compute formulas for the Lipschitz constant of several loss functions, and through extensive experimentation, demonstrate the strength of our approach using several architectures and datasets. In addition, we detail the computation of learning rates when other optimizers, namely, SGD with momentum, RMSprop, and Adam, are used. Compared to standard choices of learning rates, our approach converges faster, and yields better results.

Quantum Ergodicity in the Many-Body Localization Problem.

Abstract: We generalize Page's result on the entanglement entropy of random pure states to the many-body eigenstates of realistic disordered many-body systems subject to long-range interactions. This extension leads to two principal conclusions: first, for increasing disorder the ``shells'' of constant energy supporting a system's eigenstates fill only a fraction of its full Fock space and are subject to intrinsic correlations absent in synthetic high-dimensional random lattice systems. Second, in all regimes preceding the many-body localization transition individual eigenstates are thermally distributed over these shells. These results, corroborated by comparison to exact diagonalization for an SYK model, are at variance with the concept of ``nonergodic extended states'' in many-body systems discussed in the recent literature.

Fast Hybrid Schemes for Fractional Riccati Equations (Rough Is Not So Tough)

Abstract: We solve a family of fractional Riccati equations with constant (possibly complex) coefficients. These equations arise, for example, in fractional Heston stochastic volatility models, which have re...

On the modified logarithmic Sobolev inequality for the heat-bath dynamics for 1D systems

Abstract: The mixing time of Markovian dissipative evolutions of open quantum many-body systems can be bounded using optimal constants of certain quantum functional inequalities, such as the modified logarithmic Sobolev constant. For classical spin systems, the positivity of such constants follows from a mixing condition for the Gibbs measure via quasi-factorization results for the entropy. Inspired by the classical case, we present a strategy to derive the positivity of the modified logarithmic Sobolev constant associated with the dynamics of certain quantum systems from some clustering conditions on the Gibbs state of a local, commuting Hamiltonian. In particular, we show that for the heat-bath dynamics of 1D systems, the modified logarithmic Sobolev constant is positive under the assumptions of a mixing condition on the Gibbs state and a strong quasi-factorization of the relative entropy.

Spectral estimates for finite combinations of Hermite functions and null-controllability of hypoelliptic quadratic equations

Abstract: Some recent works have shown that the heat equation posed on the whole Euclidean space is null-controllable in any positive time if and only if the control subset is a thick set. This necessary and sufficient condition for null-controllability is linked to some uncertainty principles as the Logvinenko-Sereda theorem which give limitations on the simultaneous concentration of a function and its Fourier transform. In the present work, we prove new uncertainty principles for finite combinations of Hermite functions and establish an analogue of the Logvinenko-Sereda theorem with an explicit control of the constant with respect to the energy level of the Hermite functions as eigenfunctions of the harmonic oscillator for thick control subsets. This spectral inequality allows to derive the null-controllability in any positive time from thick control regions for para-bolic equations associated with a general class of hypoelliptic non-selfadjoint quadratic differential operators. More generally, the spectral inequality for finite combinations of Hermite functions is actually shown to hold for any measurable control subset of positive Lebesgue measure, and some quantitative estimates of the constant with respect to the energy level are given for two other classes of control subsets including the case of non-empty open control subsets.

Clustering in a hyperbolic model of complex networks

Abstract: In this paper we consider the clustering coefficient, and clustering function in a random graph model proposed by Krioukov et al. in 2010. In this model, nodes are chosen randomly inside a disk in the hyperbolic plane and two nodes are connected if they are at most at a certain hyperbolic distance from each other. It has been previously shown that this model has various properties associated with complex networks, including a power-law degree distribution, “short distances” and a non-vanishing clustering coefficient. The model is specified using three parameters: the number of nodes $n$, which we think of as going to infinity, and $\alpha , u > 0$, which we think of as constant. Roughly speaking, the parameter $\alpha $ controls the power law exponent of the degree sequence and $ u $ the average degree. Here we show that the clustering coefficient tends in probability to a constant $\gamma $ that we give explicitly as a closed form expression in terms of $\alpha , u $ and certain special functions. This improves earlier work by Gugelmann et al., who proved that the clustering coefficient remains bounded away from zero with high probability, but left open the issue of convergence to a limiting constant. Similarly, we are able to show that $c(k)$, the average clustering coefficient over all vertices of degree exactly $k$, tends in probability to a limit $\gamma (k)$ which we give explicitly as a closed form expression in terms of $\alpha , u $ and certain special functions. We are able to extend this last result also to sequences $(k_{n})_{n}$ where $k_{n}$ grows as a function of $n$. Our results show that $\gamma (k)$ scales differently, as $k$ grows, for different ranges of $\alpha $. More precisely, there exists constants $c_{\alpha , u }$ depending on $\alpha $ and $ u $, such that as $k \to \infty $, $\gamma (k) \sim c_{\alpha , u } \cdot k^{2 - 4\alpha }$ if $\frac {1}{2} \frac {3}{4}$. These results contradict a claim of Krioukov et al., which stated that $\gamma (k)$ should always scale with $k^{-1}$ as we let $k$ grow.

Training Robust Neural Networks Using Lipschitz Bounds

Abstract: Due to their susceptibility to adversarial perturbations, neural networks (NNs) are hardly used in safety-critical applications. One measure of robustness to such perturbations in the input is the Lipschitz constant of the input-output map defined by an NN. In this letter, we propose a framework to train multi-layer NNs while at the same time encouraging robustness by keeping their Lipschitz constant small, thus addressing the robustness issue. More specifically, we design an optimization scheme based on the Alternating Direction Method of Multipliers that minimizes not only the training loss of an NN but also its Lipschitz constant resulting in a semidefinite programming based training procedure that promotes robustness. We design two versions of this training procedure. The first one includes a regularizer that penalizes an accurate upper bound on the Lipschitz constant. The second one allows to enforce a desired Lipschitz bound on the NN at all times during training. Finally, we provide two examples to show that the proposed framework successfully increases the robustness of NNs.

On Mixing of Markov Chains: Coupling, Spectral Independence, and Entropy Factorization.

Abstract: For general spin systems, we prove that a contractive coupling for any local Markov chain implies optimal bounds on the mixing time and the modified log-Sobolev constant for a large class of Markov chains including the Glauber dynamics, arbitrary heat-bath block dynamics, and the Swendsen-Wang dynamics. This reveals a novel connection between probabilistic techniques for bounding the convergence to stationarity and analytic tools for analyzing the decay of relative entropy. As a corollary of our general results, we obtain $O(n\log{n})$ mixing time and $\Omega(1/n)$ modified log-Sobolev constant of the Glauber dynamics for sampling random $q$-colorings of an $n$-vertex graph with constant maximum degree $\Delta$ when $q > (11/6 - \epsilon_0)\Delta$ for some fixed $\epsilon_0>0$. We also obtain $O(\log{n})$ mixing time and $\Omega(1)$ modified log-Sobolev constant of the Swendsen-Wang dynamics for the ferromagnetic Ising model on an $n$-vertex graph of constant maximum degree when the parameters of the system lie in the tree uniqueness region. At the heart of our results are new techniques for establishing spectral independence of the spin system and block factorization of the relative entropy. On one hand we prove that a contractive coupling of a local Markov chain implies spectral independence of the Gibbs distribution. On the other hand we show that spectral independence implies factorization of entropy for arbitrary blocks, establishing optimal bounds on the modified log-Sobolev constant of the corresponding block dynamics.

A new soft computing approach for studying the wire coating dynamics with Oldroyd 8-constant fluid

Abstract: In this paper, a mathematical model for wire coating in the presence of pressure type die along with the bath of Oldroyd 8-constant fluid is presented. The model is governed by a partial differential equation, transformed into a nonlinear ordinary differential equation in dimensionless form through similarity transformations. We have designed a novel soft computing paradigm to analyze the governing mathematical model of wire coating by defining weighted Legendre polynomials based on Legendre neural networks (LeNN). Training of design neurons in the network is carried out globally by using the whale optimization algorithm (WOA) hybrid with the Nelder–Mead (NM) algorithm for rapid local convergence. Designed scheme (LeNN-WOA-NM algorithm) is applied to study the effect of variations in dilating constant (α), pressure gradient (Ω), and pseudoplastic constant β on velocity profile w(r) of fluid. To validate the proposed technique's efficiency, solutions and absolute errors are compared with the particle swarm optimization algorithm. Graphical and statistical performance of fitness value, absolute errors, and performance measures in terms of minimum, mean, median, and standard deviations further establishes the worth of the designed scheme for variants of the wire coating process.

Minimax rates in sparse, high-dimensional change point detection

Abstract: We study the detection of a sparse change in a high-dimensional mean vector as a minimax testing problem. Our first main contribution is to derive the exact minimax testing rate across all parameter regimes for n independent, p-variate Gaussian observations. This rate exhibits a phase transition when the sparsity level is of order ploglog(8n) and has a very delicate dependence on the sample size: in a certain sparsity regime, it involves a triple iterated logarithmic factor in n. Further, in a dense asymptotic regime, we identify the sharp leading constant, while in the corresponding sparse asymptotic regime, this constant is determined to within a factor of 2. Extensions that cover spatial and temporal dependence, primarily in the dense case, are also provided.

Holographic complexity of rotating black holes

Abstract: Within the framework of the “complexity equals action” and “complexity equals volume” conjectures, we study the properties of holographic complexity for rotating black holes. We focus on a class of odd-dimensional equal-spinning black holes for which considerable simplification occurs. We study the complexity of formation, uncovering a direct connection between complexity of formation and thermodynamic volume for large black holes. We consider also the growth-rate of complexity, finding that at late-times the rate of growth approaches a constant, but that Lloyd’s bound is generically violated.

CLIP: Cheap Lipschitz Training of Neural Networks

Abstract: Despite the large success of deep neural networks (DNN) in recent years, most neural networks still lack mathematical guarantees in terms of stability. For instance, DNNs are vulnerable to small or even imperceptible input perturbations, so called adversarial examples, that can cause false predictions. This instability can have severe consequences in applications which influence the health and safety of humans, e.g., biomedical imaging or autonomous driving. While bounding the Lipschitz constant of a neural network improves stability, most methods rely on restricting the Lipschitz constants of each layer which gives a poor bound for the actual Lipschitz constant.