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Related papers: Numerical Schubert Calculus via the Littlewood-Ric…

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We give integral formulas to approximate solutions of Dirichlet and Neumann problems for Helmholtz equation at high frequencies. These approximations are valid in the complementary of a union of convex compact obstacles. The first step of…

Analysis of PDEs · Mathematics 2014-02-18 François Cuvelier

We illuminate the relation between the Bruhat order on the symmetric group and structure constants (Littlewood-Richardson coefficients) for the cohomology of the flag manifold in terms of its basis of Schubert classes. Equivalently, the…

alg-geom · Mathematics 2016-11-08 Nantel Bergeron , Frank Sottile

This paper presents a rigorous numerical framework for computing multiple solutions of semilinear elliptic problems by spatiotemporal high-index saddle dynamics (HiSD), which extends the traditional HiSD to the continuous-in-space setting,…

Numerical Analysis · Mathematics 2026-01-14 Lei Zhang , Xiangcheng Zheng , Shangqin Zhu

The necessary and sufficient Horn inequalities which determine the non-vanishing Littlewood-Richardson coefficients in the cohomology of a Grassmannian are recursive in that they are naturally indexed by non-vanishing Littlewood-Richardson…

Algebraic Geometry · Mathematics 2010-03-29 Kevin Purbhoo , Frank Sottile

The ring of symmetric functions $\Lambda$, with natural basis given by the Schur functions, arise in many different areas of mathematics. For example, as the cohomology ring of the grassmanian, and as the representation ring of the…

Combinatorics · Mathematics 2009-09-03 Robin Langer

The classical Littlewood-Richardson coefficients C(lambda,mu,nu) carry a natural $S_3$ symmetry via permutation of the indices. Our "carton rule" for computing these numbers transparently and uniformly explains these six symmetries;…

Combinatorics · Mathematics 2010-02-18 Hugh Thomas , Alexander Yong

An exact, number-conserving solution to the generalized, orbit-dependent pairing problem is derived by introducing an infinite-dimensional algebra. A method for obtaining eigenvalues and eigenvectors of the corresponding Hamiltonian is also…

Nuclear Theory · Physics 2009-10-30 Feng Pan , J. P. Draayer , W. E. Ormand

We develop an accelerated gradient descent algorithm on the Grassmann manifold to compute the subspace spanned by a number of leading eigenvectors of a symmetric positive semi-definite matrix. This has a constant cost per iteration and a…

Optimization and Control · Mathematics 2024-06-27 Foivos Alimisis , Simon Vary , Bart Vandereycken

Let $\bigwedge_1$ and $\bigwedge_2$ be two symmetric function algebras in independent sets of variables. We define vector space bases of $\bigwedge_1 \otimes_\mathbb{Z} \bigwedge_2$ coming from certain quivers, with vertex sets indexed by…

The Schubert vanishing problem is a central decision problem in algebraic combinatorics and Schubert calculus, with applications to representation theory and enumerative algebraic geometry. The problem has been studied for over 50 years in…

Combinatorics · Mathematics 2025-04-07 Igor Pak , Colleen Robichaux

For finite-dimensional problems, stochastic approximation methods have long been used to solve stochastic optimization problems. Their application to infinite-dimensional problems is less understood, particularly for nonconvex objectives.…

Optimization and Control · Mathematics 2021-01-14 Caroline Geiersbach , Teresa Scarinci

The paper investigates two inertial extragradient algorithms for seeking a common solution to a variational inequality problem involving a monotone and Lipschitz continuous mapping and a fixed point problem with a demicontractive mapping in…

Optimization and Control · Mathematics 2023-08-08 Bing Tan , Liya Liu , Xiaolong Qin

We present a quantum algorithm for simulating a family of Markovian master equations that can be realized through a probabilistic application of unitary channels and state preparation. Our approach employs a second-order product formula for…

Quantum Physics · Physics 2024-07-02 Evan Borras , Milad Marvian

This paper concerns models and convergence principles for dealing with stochasticity in a wide range of algorithms arising in nonlinear analysis and optimization in Hilbert spaces. It proposes a flexible geometric framework within which…

Optimization and Control · Mathematics 2026-02-17 Patrick L. Combettes , Javier I. Madariaga

A quasi-Newton method with cubic regularization is designed for solving Riemannian unconstrained nonconvex optimization problems. The proposed algorithm is fully adaptive with at most ${\cal O} (\epsilon_g^{-3/2})$ iterations to achieve a…

Optimization and Control · Mathematics 2024-02-21 Mauricio S. Louzeiro , Gilson N. Silva , Jinyun Yuan , Daoping Zhang

A general primal-dual splitting algorithm for solving systems of structured coupled monotone inclusions in Hilbert spaces is introduced and its asymptotic behavior is analyzed. Each inclusion in the primal system features compositions with…

Optimization and Control · Mathematics 2013-02-14 Patrick L. Combettes

We consider a Cauchy problem for a (first-order) path-dependent Hamilton--Jacobi equation with coinvariant derivatives and a right-end boundary condition. Such problems arise naturally in the study of properties of the value functional in…

Optimization and Control · Mathematics 2024-12-24 Mikhail I. Gomoyunov

Overdetermined systems of first kind integral equations appear in many applications. When the right-hand side is discretized, the resulting finite-data problem is ill-posed and admits infinitely many solutions. We propose a numerical method…

Numerical Analysis · Mathematics 2023-07-26 Patricia Díaz de Alba , Luisa Fermo , Federica Pes , Giuseppe Rodriguez

We provide a polynomial time algorithm for computing the universal Gr\"obner basis of any polynomial ideal having a finite set of common zeros in fixed number of variables. One ingredient of our algorithm is an effective construction of the…

Combinatorics · Mathematics 2007-05-23 Eric Babson , Shmuel Onn , Rekha Thomas

Optimization under the symplecticity constraint is an approach for solving various problems in quantum physics and scientific computing. Building on the results that this optimization problem can be transformed into an unconstrained problem…

Optimization and Control · Mathematics 2024-06-21 Bin Gao , Nguyen Thanh Son , Tatjana Stykel