New estimator for SDEs is shown to be an adjoint state method.
problem Estimating gradients for overparameterized SDEs efficiently.
method Demonstrates generator gradient estimator as an adjoint state method.
result Generator gradient estimator is an adjoint state method for SDEs.
Two of the most important areas in computational finance: Greeks and, respectively, calibration, are based on efficient and accurate computation of a large number of sensitivities. This paper gives an overview of adjoint and automatic differentiation (AD), also known as algorithmic differentiation, techniques to calcul…
Derives backward differentiation for Bermudan product valuation.
problem Valuation of Bermudan products using conditional expectation.
method Three properties for backward differentiation of algorithms with conditional expectation.
result Clean and simple implementation of backward differentiation.
Proves formal self-adjointness of certain differential operators.
problem Verifying conjectures about differential operators.
method Proving formal self-adjointness through mathematical proof.
result Proves two conjectures about differential operators.
The study confirms essential self-adjointness for certain differential operators on manifolds.
problem Essential self-adjointness of differential operators on closed manifolds.
method Analyzing the Hamiltonian flow of the symbol of differential operators.
result The conjecture that certain differential operators are essentially self-adjoint if their Hamiltonian flow is complete.
Framework for pricing waterfall structures using simulation and uncertainty modeling.
problem Pricing complex structured finance instruments under uncertainty.
method Simulation-based uncertainty modeling, calibrated probability distributions, PyTorch implementation, Adjoint Algorithmic Differentiation (AAD).
result Efficient gradient computation for risk sensitivity analysis and optimization.
We describe a canonical form for linear differential operators that are formally self-adjoint or formally skew-adjoint.
Essential self-adjointness proven for powers of first-order differential operators on non-compact manifolds with low-regularity metrics.
problem Essential self-adjointness of powers of first-order differential operators on non-compact manifolds with low-regularity metrics.
method Demonstrates the equivalence between essential self-adjointness and a negligible boundary property for first-order differential operators with locally bounded measurable coefficients. For higher regularity coefficients, essential self-adjointness of higher powers is shown under the same condition.
result Essential self-adjointness of first-order differential operators and their higher powers proven under specific conditions on non-compact manifolds with low-regularity metrics.
Proposes DAM for optimizing discrete generative models.
problem Challenges in optimizing discrete generative models.
method Discrete Adjoint Matching (DAM) for discrete state spaces.
result Demonstrates effectiveness on synthetic and mathematical reasoning tasks.
Derives adjoint formulas for matrix operations and applies them to specific cases.
problem Computing adjoints for matrix operations and specific matrix types.
method Derives adjoint formulas for matrix operations and applies them to specific cases.
result Closed-form expressions for adjoints in specific matrix types.
The paper explores self-adjointness of Laplace-Beltrami operator on special geometric manifolds.
problem Characterizing self-adjoint extensions of the Laplace-Beltrami operator on α-Grushin manifolds. method Introducing an exotic calculus of pseudodifferential operators adapted to the geometry of the singularity.
result Criterion for essential self-adjointness and determination of several self-adjoint extensions.
This work discusses AAD for financial model calibration and its parallelization benefits.
problem Calibrating stochastic financial models using Automatic Adjoint Differentiation.
method Demonstrates the use of Automatic Adjoint Differentiation for functions in financial models and its parallelization potential.
result Theoretical and numeric results show that AAD allows perfect SIMD parallelization and is efficient.
Let Δ be a linear differential operator acting on the space of densities of a given weight $\lo$ on a manifold M. One can consider a pencil of operators $\hPi(Δ)=\{Δ_ł\}$ passing through the operator Δ such that any Δł is a linear differential operator acting on densities of weight ł. This pencil can be iden…
We use AD to compute gradients for complex functionals in stochastic model calibration.
problem Computing gradients for functions involving expectations in stochastic models.
method Automatic Adjoint Differentiation and parallelization.
result Faster and easier to implement approaches for gradient computation.
Let M be a complete Riemannian manifold and let Ω∗(M) denote the space of differential forms on M. Let d:Ω∗(M)→Ω∗+1(M) be the exterior differential operator and let $\Del=dd^*+d^*d$ be the Laplacian. We establish a sufficient condition for the Schroedinger operator $H=\Del+V(x)$ (where the potential $V…
We show how Adjoint Algorithmic Differentiation (AAD) allows an extremely efficient calculation of correlation Risk of option prices computed with Monte Carlo simulations. A key point in the construction is the use of binning to simultaneously achieve computational efficiency and accurate confidence intervals. We illus…
The paper presents a method to infer unknown forcing functions in differential equations using Gaussian processes and adjoints.
problem Inferring unknown forcing functions in differential equations from noisy observations.
method Using adjoint methods to efficiently infer Gaussian process (GP) driven differential equations, with truncated basis expansions of the GP kernel.
result Efficient Bayesian inference of forcing functions modeled as GPs using adjoints, with lower computation than MCMC methods.
Abstracts a construction of boundary triplets for self-adjoint elliptic problems.
problem Computing the index of families of self-adjoint elliptic boundary problems.
method Abstract axiomatic version of boundary triplets and their applications.
result Analytic proof of index theorem and computation of index differences.
Differential ML combines AAD with ML for fast, accurate financial derivatives pricing and risk management.
problem Computational bottlenecks in financial derivatives risk management.
method Novel algorithms using automatic adjoint differentiation (AAD) for training fast, accurate approximations in real-time.
result Convergence guarantees for fast, accurate pricing and risk approximations for arbitrary derivatives instruments.
The purpose of this note is to present several criteria for essential self-adjointness. The method is based on ideas due to Shubin. This note is divided into two parts. The first part deals with symmetric first order systems on the line in the most general setting. Such a symmetric first order system of differential eq…
The well known conformal covariance of the Dirac operator acting on spinor fields over a semi Riemannian spin manifold does not extend to powers thereof in general. For odd powers one has to add lower order curvature correction terms in order to obtain conformal covariance. We derive an algorithmic construction in term…
Efficiently differentiate functions of large matrices using new adjoint systems.
problem Differentiating functions of large matrices in scientific and probabilistic machine learning models.
method Deriving and implementing new adjoint systems for Lanczos and Arnoldi iterations in JAX.
result Efficient differentiation of PDEs, Gaussian process models, and Bayesian neural networks.
SDE Matching eliminates simulation for training Latent SDEs, achieving similar performance.
problem Training Latent SDEs with adjoint sensitivity methods is computationally expensive and limited.
method SDE Matching, inspired by Score- and Flow Matching, eliminates simulation for training Latent SDEs.
result SDE Matching achieves performance comparable to adjoint sensitivity methods while reducing computational complexity.
Paper discusses how automatic differentiation aids financial markets in computing derivatives.
problem Computing derivatives for financial market participants to understand exposure to market moves.
method Uses automatic differentiation and adjoint algorithmic differentiation (AAD) to compute financial sensitivities.
result Demonstrates the limitations of AAD and the need for specialized tools in financial contexts.
New method efficiently computes gradients for stochastic differential equations.
problem Computing gradients for stochastic differential equations efficiently.
method Generalized adjoint sensitivity method to stochastic differential equations.
result Time-efficient and memory-efficient computation of gradients with high-order solvers.
SNAPO optimizes policies for complex sequential decisions using differentiable simulation.
problem Optimizing policies for high-dimensional, sequential decisions under uncertainty.
method Embeds neural policy in a differentiable simulator, computes gradients efficiently.
result Produces sensitivities at a cost proportional to one reverse pass, regardless of sensitivity count.
New method reduces errors in pricing and sensitivities for discontinuous payoffs.
problem Errors in pricing and sensitivities for discontinuous payoffs in digital and barrier options.
method Alternative methods for estimating sensitivities, including likelihood ratio and hybrid methods.
result New methods substantially reduce test errors in prices and sensitivities.
Study compares spectra of differential operators on Riemannian coverings.
problem Comparing spectra of differential operators on Riemannian coverings.
method Analyzes the spectrum of differential operators on bundles under Riemannian coverings.
result Spectrum of D1 is contained in the essential spectrum of D2 under certain conditions. Researchers create a parametrix for resolvents on manifolds with ends.
problem Essential self-adjointness of elliptic symmetric differential operators on manifolds with ends.
method Introduced semiclasical pseudodifferential operators compatible with the end structure.
result Essential self-adjointness of elliptic symmetric differential operators proved.
Study essential spectrum of differential operators on geometrically finite orbifolds.
problem Analyzing the essential spectrum of differential operators over specific geometric structures.
method Investigates first order and Laplace type elliptic differential operators on Riemannian vector bundles over geometrically finite orbifolds.
result Discovers properties of essential spectra for these operators.
Essential self-adjointness and spectrum of CR GJMS operator proved.
problem Characterizing the spectrum of CR GJMS operator.
method Proving essential self-adjointness and closed range, analyzing spectrum.
result CR GJMS operator has discrete spectrum with finite-dimensional eigenspaces.
Paper proves index theorem for self-adjoint elliptic boundary problems.
problem Proving index theorem for self-adjoint elliptic boundary problems.
method Topological and pseudo-differential methods, generalized Atiyah-Singer approach.
result Removed technical assumption to prove index theorem.
Investigates the relationship between ResNets and Neural ODEs, quantifying their closeness and providing training methods.
problem Quantifying the distance between ResNet dynamics and Neural ODE solutions.
method Bounding the distance between hidden state trajectories and Neural ODE solutions, using gradient descent and Heun's method.
result Gradient descent and Heun's method can implicitly regularize ResNets towards Neural ODEs, especially for smooth residual functions.
This paper is being replaced by another of the author's that contains a brief summary of the problem of positivity of Green's functions, heat kernels, and principal eigenvalues of higher-order elliptic differential operators.
The fibre bundles adjoint to generalized almost quaternionic structures are studied. The most important classes of generalized almost quaternionic manifolds are considered.
Neural controlled DEs model irregular time series by adjusting based on observations.
problem Modeling irregularly sampled multivariate time series with memory-efficient adjoint-based backpropagation.
method Neural controlled differential equations (CDEs) that adjust based on subsequent observations.
result Achieves state-of-the-art performance on various datasets.
BMS conjecture on geodesic manifolds posits square integrable solutions for certain operators.
problem Positivity of solutions for a specific class of differential operators on geodesic manifolds.
method Analyzes connections to essential self-adjointness of covariant Schrödinger operators.
result The conjecture remains unresolved for over 14 years.
The paper studies Lie n-algebroids and their representations up to homotopy.
problem Understanding Lie n-algebroids and their representations.
method Analyzes differential graded modules and representations up to homotopy, describes adjoint and coadjoint modules, and computes the Weil algebra.
result Alternative characterisation of non-degeneracy of higher Poisson structures.
The Plebański complex is a differential operator that squares to the Laplacian and is composed of two Dirac operators.
problem The Plebański complex studies the linearization of equations for hyper-Kähler manifolds.
method Defined and studied properties of the Plebański complex, showing it fits into the elliptic complex framework.
result The Plebański complex is an elliptic differential operator that squares to the Laplacian and is composed of two Dirac operators.
Study spectral properties of modified Dirichlet-to-Neumann map on differential forms.
problem Spectral properties of modified Dirichlet-to-Neumann map on differential forms.
method Investigation of self-adjointness and purely discrete spectrum of the operator Λ on coclosed forms.
result Hersch-Payne-Schiffer type inequality relating eigenvalues of Λ to eigenvalues of Hodge Laplacian on the boundary.
ACA method improves gradient estimation for neural ODEs, reducing error and training time.
problem Inaccurate gradient estimation methods hinder the performance of neural ODEs on benchmark tasks.
method Adaptive Checkpoint Adjoint (ACA) method that applies trajectory checkpointing, deletes redundant components, and supports adaptive solvers.
result ACA reduces error rate by half and training time by half compared to adjoint and naive methods on image classification tasks.
We formulate higher order variations of a Lagrangian in the geometric framework of jet prolongations of fibered manifolds. Our formalism applies to Lagrangians which depend on an arbitrary number of independent and dependent variables, together with higher order derivatives. In particular, we show that the second varia…
Develops a categorified excision principle for elliptic symbol families.
problem Expressing the index class in K-theory using differential-topological data.
method Categorical index calculus, excision principle.
result Allows comparison of categorified index problems on different manifolds.
Researchers found a canonical form for pairs of Hermitian and antilinear operators.
problem Simultaneous normalization of pairs of Hermitian and antilinear operators in differential geometry.
method Finding a canonical form for pairs of Hermitian and antilinear operators.
result Generalized previous results on simultaneous normalization of such pairs.
Revisits gravitational wave equations using differential duality.
problem Linearizing Einstein equations for gravitational waves.
method Introduces linear transformations and uses differential duality.
result Reveals the formal adjoint relationship between Ricci and Einstein operators.
Derives new symmetry operators for linearized gravity.
problem Symmetries of linearized gravity on vacuum backgrounds.
method Operator identities and adjoints, Teukolsky equation, separability.
result New symmetry operators of differential orders 4 and 6.
Introduces Hom-Lie groups and their integrability, defining Hexp map and adjoint representation.
problem Integrability of Hom-Lie algebras and associated Hom-Lie groups.
method Definition of Hom-Lie groups and algebras, integration of Hom-Lie algebras, Hexp map definition.
result Every regular Hom-Lie algebra is integrable, Hexp map is universal.
Framework calculates positional influence in causal residual Transformers.
problem Understanding positional influence in causal residual Transformers.
method Adjoint-sensitivity framework for positional influence in causal residual Transformers.
result Exact evolution of adjoint-energy influence density and decomposition into residual transmission, nonlocal Volterra, and local channels.