Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.33634

Approximate Controllability of Neutral Integro-Differential Inclusions Associated with Hemivariational Inequalities in Banach Spaces with Uniformly Convex Dual

Jyoti Jindal·Sumit Arora·Manil T. Mohan
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this paper, we investigate the approximate controllability of neutral integro-differential inclusions driven by hemivariational inequalities with nonlocal inclusion conditions in a Banach space $\mathbb{V}$ whose dual $\mathbb{V}^*$ is uniformly convex, using resolvent operator theory. We first establish an equivalence between the original problem and an associated differential inclusion involving the Clarke subdifferential. Under suitable assumptions, we then prove the existence of mild solutions corresponding to admissible controls in $\mathrm{L}^2(I,\mathbb{U})$. A key contribution of this work lies in the analysis of the control construction in Banach spaces, where the duality mapping $\mathcal{J} = \partial\big(\tfrac{1}{2}\|\cdot\|_{\mathbb{V}}^2\big)$ is generally nonlinear, in contrast to the Hilbert space setting, while such nonlinearity has been viewed as a technical obstruction in earlier studies. Based on this framework, we prove approximate controllability of the original system by relating it to the controllability of the associated linearized inclusion and constructing appropriate controls involving the duality mapping. Finally, we present applications to illustrate the effectiveness of the proposed approach.

Explore related subjects

math.OC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jyoti Jindal, Sumit Arora, Manil T. Mohan. 2026-10-24. Approximate Controllability of Neutral Integro-Differential Inclusions Associated with Hemivariational Inequalities in Banach Spaces with Uniformly Convex Dual. https://arxiv.org/abs/2610.33634

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Mini-Batch Stochastic Conjugate Gradient Algorithms with Minimal Variance

Stochastic gradient descent (SGD) methods have gained widespread popularity for solving large-scale optimization problems. However, the inherent variance in SGD often leads to slow convergence rates. We introduce a family of unbiased stochastic gradient estimators that encompasses existing estimators from the literature and identify a gradient estimator that not only maintains unbiasedness but also achieves minimal variance. Compared with the existing estimator used in SGD algorithms, the proposed estimator demonstrates a significant reduction in variance. By utilizing this stochastic gradient estimator to approximate the full gradient, we propose two mini-batch stochastic conjugate gradient algorithms with minimal variance. Under the assumptions of strong convexity and smoothness on the objective function, we prove that the two algorithms achieve linear convergence rates. Numerical experiments validate the effectiveness of the proposed gradient estimator in reducing variance and demonstrate that the two stochastic conjugate gradient algorithms exhibit accelerated convergence rates and enhanced stability.

math.OC↗
arXiv2026

Analysis of Primal-Dual Langevin Algorithms

We analyze a recently proposed class of algorithms for the problem of sampling from probability distributions $μ^\ast$ in $\mathbb{R}^d$ with a Lebesgue density of the form $μ^\ast(x) \propto \exp(-f(Kx)-g(x))$, where $K$ is a linear operator and $f,g$ convex and non-smooth. The method is a generalization of the primal-dual hybrid gradient optimization algorithm to a sampling scheme. We give the iteration's continuous time limit, a stochastic differential equation in the joint primal-dual variable, and its mean field limit Fokker-Planck equation. Under mild conditions, the scheme converges to a unique stationary state in continuous and discrete time. Contrary to purely primal overdamped Langevin diffusion, the stationary state in continuous time does not have $μ^\ast$ as its primal marginal. Thus, further analysis is carried out to bound the bias induced by the partial dualization, and potentially correct for it in the diffusion. Time discretizations of the diffusion lead to implementable algorithms, but, as is typical in Langevin Monte Carlo methods, introduce further bias. We prove bounds for these discretization errors, which allow to give convergence results relating the produced samples to the target. We demonstrate our findings numerically first on small-scale examples in which we can exactly verify the theoretical results, and subsequently on typical examples of larger scale from Bayesian imaging inverse problems.

math.OC↗
arXiv2026

An Efficient Parallel Solver for Linear-Equality-Constrained LQR Problems

We present a parallel solver for finite-horizon linear-quadratic regulator (LQR) problems with arbitrary stagewise linear equality constraints, using linear work and logarithmic depth in the horizon for fixed local dimensions. The solver uses a two-part reduction that separates feasibility from optimization. First, an associative suffix scan computes the states from which each remaining horizon is feasible. Second, we use these feasible-continuation sets to eliminate the constraints independently at each stage, producing an unconstrained LQR problem with smaller or equal, possibly stage-varying dimensions. A parallel unconstrained-LQR solver completes the primal solution. Finally, we recover valid multipliers from the original KKT stationarity equations using a logarithmic-depth contraction-expansion procedure, even when the multipliers are not unique. Sequential and parallel implementations are validated against dense KKT solutions and original residuals. We release a C++/CUDA implementation with JAX bindings under the MIT license.

math.OC↗