Abstract: In this paper, a new fractional operator of variable order with the use of the monotonic increasing function is proposed in sense of Caputo type. The properties in term of the Laplace and Fourier transforms are analyzed and the results for the anomalous diffusion equations of variable order are discussed. The new formulation is efficient in modeling a class of concentrations in the complex transport process.

Abstract: Instrumental variables (IVs) are commonly used to estimate the effects of some treatments. A valid IV should be as good as randomly assigned, it should not have a direct effect on the outcome, and it should not induce any unit to forgo treatment. This last condition, the so‐called monotonicity condition, is often implausible. This paper starts by showing that actually, IVs are still valid under a weaker condition than monotonicity. It then derives conditions that are sufficient for this weaker condition to hold and whose plausibility can easily be assessed in applications. It finally reviews several applications where this weaker condition is applicable while monotonicity is not. Overall, this paper extends the applicability of the IV estimation method. Monotonicity defiers instrumental variable average treatment effect partial identification C21 C26

Abstract: Phase transitions play an important role in understanding search difficulty in combinatorial optimisation. However, previous attempts have not revealed a clear link between fitness landscape properties and the phase transition. We explore whether the global landscape structure of the number partitioning problem changes with the phase transition. Using the local optima network model, we analyse a number of instances before, during, and after the phase transition. We compute relevant network and neutrality metrics; and importantly, identify and visualise the funnel structure with an approach (monotonic sequences) inspired by theoretical chemistry. While most metrics remain oblivious to the phase transition, our results reveal that the funnel structure clearly changes. Easy instances feature a single or a small number of dominant funnels leading to global optima; hard instances have a large number of suboptimal funnels attracting the search. Our study brings new insights and tools to the study of phase transitions in combinatorial optimisation.

Abstract: We propose novel finite-dimensional spaces of well-behaved $\mathbb {R}^n\rightarrow \mathbb {R}^n$ transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and supports convenient modeling choices such as smoothing priors and coarse-to-fine analysis. Importantly, the proposed approach, partly due to its rapid likelihood evaluations and partly due to its other properties, facilitates tractable inference over rich transformation spaces, including using Markov-Chain Monte-Carlo methods. Its applications include, but are not limited to: monotonic regression (more generally, optimization over monotonic functions); modeling cumulative distribution functions or histograms; time-warping; image warping; image registration; real-time diffeomorphic image editing; data augmentation for image classifiers. Our GPU-based code is publicly available.

Abstract: In data science applications, it is very often to require predictive models satisfying monotonicity with respect to the explanatory variables involved in the dataset. In ordinal classification or regression, this occurs when the output variable or class label do not decrease when input variables increase, or vice versa. This problem is commonly known as monotonic classification, and most existing classification techniques are not able to manage this kind of constraints or they require first to monotonize the data. In the literature, the monotonicity has been considered in linguistic fuzzy models, fuzzy-inference methods, and fuzzy rule-based control systems. However, to the best of our knowledge, there is no fuzzy rule-based system designed to produce monotonic fuzzy rule-based models for classification problems. In this paper, we propose to incorporate some mechanisms based on monotonicity indexes for addressing such problems in two popular and competitive evolutionary fuzzy systems algorithms for classification and regression tasks: FARC-HD and FSmogfs $^e$ + Tun $^e$ . In addition, the proposals are able to handle any kind of classification dataset without the necessity of preprocessing. The quality of our approaches is analyzed using statistical analysis and comparing with well-known monotonic classifiers.

Abstract: In the paper, the authors present an explicit form for a family of inhomogeneous linear ordinary differential equations, find a more significant expression for all derivatives of a function related to the solution to the family of inhomogeneous linear ordinary differential equations in terms of the Lerch transcendent, establish an explicit formula for computing all derivatives of the solution to the family of inhomogeneous linear ordinary differential equations, acquire the absolute monotonicity, complete monotonicity, the Bernstein function property of several functions related to the solution to the family of inhomogeneous linear ordinary differential equations, discover a diagonal recurrence relation of the Stirling numbers of the first kind, and derive an inequality for bounding the logarithmic function.

Abstract: We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network. We implement the layers and projections with new computational graph nodes in TensorFlow and use the ADAM optimizer and batched stochastic gradients. Experiments on benchmark and real-world datasets show that six-layer monotonic deep lattice networks achieve state-of-the art performance for classification and regression with monotonicity guarantees.

Abstract: We give a comprehensive treatment of the parabolic Signorini problem based on a generalization of Almgren's monotonicity of the frequency. This includes the proof of the optimal regularity of solutions, classication of free boundary points, the regularity of the regular set and the structure of the singular set.

Abstract: In this work, we investigate the accelerated proximal gradient method for nonconvex programming (APGnc). The method compares between a usual proximal gradient step and a linear extrapolation step, and accepts the one that has a lower function value to achieve a monotonic decrease. In specific, under a general nonsmooth and nonconvex setting, we provide a rigorous argument to show that the limit points of the sequence generated by APGnc are critical points of the objective function. Then, by exploiting the Kurdyka-Łojasiewicz (KŁ) property for a broad class of functions, we establish the linear and sub-linear convergence rates of the function value sequence generated by APGnc. We further propose a stochastic variance reduced APGnc (SVRG-APGnc), and establish its linear convergence under a special case of the KŁ property. We also extend the analysis to the inexact version of these methods and develop an adaptive momentum strategy that improves the numerical performance.

Abstract: We consider a monotonicity-type result for functions f:Na→R satisfying the sequential fractional difference inequalityΔ1+a-μνΔaμf(t)≥0,for t∈N2+a-μ-ν, where 0<μ<1, 0<ν<1, and 1<μ+ν<2. A comparison between the sequential and non-sequential settings is provided, and we note that nontrivial dissimilarities exist between the two settings. We demonstrate, in addition, that, in a certain sense, our results are almost sharp. Finally, some numerical examples are provided in order to clarify our results.

Abstract: This paper develops a dynamics-based nonsingular interval model and proposes a first-order composite function interval perturbation method (FCFIPM) for luffing angular response field analysis of the dual automobile cranes system (DACS) with narrowly bounded uncertainty. By using the nonsingular interval model to describe a structure parameter with bounded uncertainty, the reasonable lower and upper bounds can be obtained, which is quite different from the traditional interval model with approximate bounds only from a large number of samples. Firstly, for the DACS with deterministic information, the inverse kinematics is analyzed, and the dynamic model of the DACS is established based on the virtual work principle and the inverse kinematics. Secondly, considering the nonsingularity of the dynamic response curves, a dynamics-based nonsingular interval model is introduced. Based on the nonsingular interval model, the interval luffing angular response vector equilibrium equation of the DACS is established. Thirdly, a first-order composite function interval perturbation method is proposed. In the FCFIPM, the composite function vectors are expanded by using the first-order Taylor series expansion, based on the differential property of composite function and monotonic analysis technique, the lower and upper bounds of the interval luffing angular response vector of the crane 1 and crane 2 of the DACS are determined. The first case is to investigate the deterministic kinematics and dynamics of the DACS with a given trajectory. The second case is provided to illustrate the detailed implementation process of constructing a dynamics-based nonsingular interval model. Finally, some numerical examples are given to verify the feasibility and efficiency of the FCFIPM for solving the luffing angular response field problem with narrowly interval parameters.

Abstract: In the literature, three basic assumptions are used to modify monotonic constitutive models in order to simplify fatigue analysis of concrete. First, the fatigue hysteresis loop at failure ...

Abstract: We present necessary conditions for monotonicity, in one form or another, of fixed point iterations of mappings that violate the usual nonexpansive property. We show that most reasonable notions of linear-type monotonicity of fixed point sequences imply {\em metric subregularity}. This is specialized to the alternating projections iteration where the metric subregularity property takes on a distinct geometric characterization of sets at points of intersection called {\em subtransversality}. Our more general results for fixed point iterations are specialized to establish the necessity of subtransversality for consistent feasibility with a number of reasonable types of sequential monotonicity, under varying degrees of assumptions on the regularity of the sets.

Abstract: Building upon work by Matsumoto, we show that the quantum relative entropy with full-rank second argument is determined by four simple axioms: i) Continuity in the first argument, ii) the validity of the data-processing inequality, iii) additivity under tensor products, and iv) super-additivity. This observation has immediate implications for quantum thermodynamics, which we discuss. Specifically, we demonstrate that, under reasonable restrictions, the free energy is singled out as a measure of athermality. In particular, we consider an extended class of Gibbs-preserving maps as free operations in a resource-theoretic framework, in which a catalyst is allowed to build up correlations with the system at hand. The free energy is the only extensive and continuous function that is monotonic under such free operations.

Abstract: We generalize the Knuth–Bendix order (KBO) to higher-order terms without \(\lambda \)-abstraction. The restriction of this new order to first-order terms coincides with the traditional KBO. The order has many useful properties, including transitivity, the subterm property, compatibility with contexts (monotonicity), stability under substitution, and well-foundedness. Transfinite weights and argument coefficients can also be supported. The order appears promising as the basis of a higher-order superposition calculus.

Abstract: In this second part, we analyze the dissipation properties of Generalized Poisson-Kac (GPK) processes, considering the decay of suitable $L^2$-norms and the definition of entropy functions. In both cases, consistent energy dissipation and entropy functions depend on the whole system of primitive statistical variables, the partial probability density functions $\{ p_\alpha({\bf x},t) \}_{\alpha=1}^N$, while the corresponding energy dissipation and entropy functions based on the overall probability density $p({\bf x},t)$ do not satisfy monotonicity requirements as a function of time. Examples from chaotic advection (standard map coupled to stochastic GPK processes) illustrate this phenomenon. Some complementary physical issues are also addressed: the ergodicity breaking in the presence of attractive potentials, and the use of GPK perturbations to mollify stochastic field equations.

Abstract: In this work we investigate integro-differential initial value problems with Riemann Liouville fractional derivatives where the forcing function is a sum of an increasing function and a decreasing function. We will apply the method of lower and upper solutions and develop two monotone iterative techniques by constructing two sequences that converge uniformly and monotonically to minimal and maximal solutions. In the first theorem we will construct two natural sequences and in the second theorem we will construct two intertwined sequences. Finally, we illustrate our results with an example.

Abstract: In this paper, a notion of partially Hausdorff measure of noncompactness in partially ordered Banach spaces is introduced, and some Krasnoselskii-type fixed point theorems under certain mixed conditions are proved. Some applications of the obtained fixed point theorems are given to a class of fractional hybrid evolution equations for proving the existence of mild solutions under certain monotonicity conditions. At the end, an example of the fractional parabolic equation is given to illustrate the abstract results.

Abstract: Summary A formal likelihood ratio hypothesis test for the validity of a parametric regression function is proposed, using a large dimensional, non-parametric double-cone alternative. For example, the test against a constant function uses the alternative of increasing or decreasing regression functions, and the test against a linear function uses the convex or concave alternative. The test proposed is exact and unbiased and the critical value is easily computed. The power of the test increases to 1 as the sample size increases, under very mild assumptions—even when the alternative is misspecified, i.e. the power of the test converges to 1 for any true regression function that deviates (in a non-degenerate way) from the parametric null hypothesis. We also formulate tests for the linear versus partial linear model and consider the special case of the additive model. Simulations show that our procedure behaves well consistently when compared with other methods. Although the alternative fit is non-parametric, no tuning parameters are involved. Supplementary materials with proofs and technical details are available on line.