A unified iterate convergence framework that applies to a broad group of kernels and composite objective functions is developed, and it is shown that the continuous-time BPM (mirror flow) converges to a stationary point for o-minimal definable objective functions, yielding the first trajectory convergence result for mirror flow.
Abstract
Iterate convergence of Bregman proximal methods (BPMs) has long remained open, especially for nonconvex objectives. Recently, \citet{chen2026skl} made progress by establishing iterate convergence for a BPM via the so-called scaled Kurdyka-\L{}ojasiewicz (SK\L{}) property, but only for the Shannon entropy kernel and linearly constrained problems. In this paper, we develop a unified iterate convergence framework that applies to a broad group of kernels and composite objective functions. Our approach extends the analytical tools in \cite{chen2026skl}, in particular the SK\L{} property, which plays a central role in ensuring convergence of the generated sequences. By introducing kernel-dependent parameterization functions, we show that the extended SK\L{} property holds for all continuous subanalytic functions, particularly when the kernel has a closed domain. We then verify that the assumptions of the framework are satisfied by standard BPMs under mild regularity conditions, thereby establishing their iterate convergence for a wide range of objective functions. Furthermore, based on the parameterization functions, we show that the continuous-time BPM (mirror flow) converges to a stationary point for o-minimal definable objective functions, yielding the first trajectory convergence result for mirror flow without imposing convexity assumptions on the objective function or isolation assumptions on stationary points. Taken together, these discrete- and continuous-time convergence results provide a unified trajectory convergence theory for BPMs.
A novel convergence analysis framework for the BPGM with the Shannon entropy kernel is developed, yielding strong convergence results for a broad class of objective functions under linear constraints.
We introduce two novel randomized iterative regularization frameworks, termed \texttt{RIGKT} and \texttt{RIAT}, for solving large-scale linear ill-posed inverse problems governed by systems of equations. The proposed methods combine randomized iterated Tikhonov regularization with Krylov subspace projection techniques, utilizing Golub--Kahan bidiagonalization for general rectangular systems (\texttt{RIGKT}) and Arnoldi decomposition for square systems (\texttt{RIAT}). Unlike existing deterministic schemes that rely on fixed iteration counts, our framework incorporates randomized equation selection, an adaptive step-size strategy, and a global, discrepancy-based a posteriori early-stopping rule tailored specifically to the stochastic setting. We present a comprehensive regularization analysis establishing Bregman-distance monotonicity, finite termination, exact-data convergence, and pathwise stability under noise. Furthermore, we prove that the stopped iterates converge almost surely and in the mean-square sense to the true solution, establishing a rigorous regularization property. To the best of our knowledge, this is the first theoretical framework to simultaneously account for randomization, Krylov-subspace dimension reduction, and implementable early stopping. Numerical experiments involving two-dimensional X-ray computed tomography (CT) and image deblurring demonstrate that \texttt{RIGKT} and \texttt{RIAT} reliably reconstruct structural features across various noise regimes.
Ravi Verma, Harshit Bajpai, Ankik Kumar Giri· arXiv.org· 0 citations
A globally convergent regularized Newton method with positive definite regularization for solving nonsmooth optimization problems that replaces the identity matrix in traditional algorithms with a general positive-definite symmetric matrix to regularize the generalized Hessian.
We make a convergence analysis on the joint bidiagonalization (JBD) method that computes several extreme generalized singular value decomposition (GSVD) components of a regular matrix pair $\{A,L\}$, and show that the right and left Ritz vectors obtained by it may converge erratically and even may fail to converge, while Ritz values converge. These convergence results hold for a class of general Rayleigh--Ritz projection methods for the GSVD problem under the hypothesis that the deviation of a desired right generalized singular vector from the right subspace tends to zero. We prove the interlacing property of Ritz values and generalized singular values, and extend it to the generalized singular values of $\{A,L\}$ and the matrix pairs consisting of subsets of its columns. To overcome the irregular convergence or possible non-convergence of the JBD method, we nontrivially extend the refined Rayleigh--Ritz projection for the eigenvalue problem to the GSVD problem, and propose a refined JBD (RJBD) method that replaces the right Ritz vectors by new approximations, called the right refined Ritz vectors, satisfying certain residual optimality; we define new approximate left generalized singular vectors, called the left refined Ritz vectors. We prove that the left and right refined Ritz vectors unconditionally converge under the same hypothesis. We extend the implicit restarting scheme to the RJBD method, and develop an implicitly restarted RJBD algorithm with the refined shifts proposed. Numerical experiments illustrate that the new algorithm is at least competitive and often considerably more efficient than the implicitly restarted JBD algorithm.
Sequence convergence to a boundary Karush--Kuhn--Tucker (KKT) point has long remained unclear for nonconvex mirror descent with Legendre kernels. The difficulty arises from the blow-up of the gradient of the Legendre kernel at the boundary. Recent work~\cite{dingtoh2026nonkkt} shows that mirror descent can accumulate at non-KKT boundary points despite decreasing objective values, precluding a convergence guarantee to KKT points in general. Despite this negative result, mirror descent remains effective in many real applications. Motivated by this contrast, we address the boundary difficulty directly and establish KKT convergence of mirror descent for a broad class of structured nonconvex problems. We analyze mirror descent in reparameterized variables, where the Hessian metric is flattened and remains nondegenerate as the boundary is approached. Under extension and definability conditions jointly coupling the objective, the Legendre kernel, and the feasible region, the reparameterized sequence has finite length and converges, thereby recovering convergence to a KKT point of the original sequence. Our general framework applies to some concrete instances: Shannon entropy, Fermi--Dirac entropy, and power kernels on polyhedron.
This work presents an efficient analytical framework based on the Natural Residual Power Series Method (NRPSM) for solving several forms of the time-fractional Kuramoto–Sivashinsky equation with the Caputo derivative. The proposed method avoids discretization and linearization while producing rapidly convergent analytical series solutions. Earlier residual power series treatments assert convergence under a contractivity assumption without verifying it for the equation at hand. We close this gap by deriving an explicit formula for the contraction constant directly from the problem data, so the convergence criterion is checkable before any computation begins. A rigorous theoretical analysis is established through explicit contraction conditions, convergence proofs in the Sobolev space H4(R), and an explicit geometric-type error estimate that quantifies how the fractional order governs the convergence rate through two competing effects, without presuming a uniform direction of influence. Stability with respect to perturbations in the initial data is also proven using a fractional Gronwall inequality. Numerical results demonstrate excellent agreement with exact and previously published solutions, achieving very small absolute errors using only a few series terms. The obtained results confirm that the NRPSM is an accurate, stable, and computationally efficient approach for nonlinear fractional evolution equations.
Z. Alqahtani, A. Hagag· Symmetry· 0 citations
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