Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Introduction to linear and nonlinear programming

We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

Deep learning on image denoising: An overview.

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minima, simple iterative methods such as gradient descent have been remarkably successful in practice. The theoretical footings, however, had been largely lacking until recently. In this tutorial-style overview, we highlight the important role of statistical models in enabling efficient nonconvex optimization with performance guarantees. We review two contrasting approaches: (1) two-stage algorithms, which consist of a tailored initialization step followed by successive refinement; and (2) global landscape analysis and initialization-free algorithms. Several canonical matrix factorization problems are discussed, including but not limited to matrix sensing, phase retrieval, matrix completion, blind deconvolution, and robust principal component analysis. Special care is taken to illustrate the key technical insights underlying their analyses. This article serves as a testament that the integrated consideration of optimization and statistics leads to fruitful research findings.

Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

This paper develops and analyzes a generalization of the Broyden class of quasi-Newton methods to the problem of minimizing a smooth objective function $f$ on a Riemannian manifold. A condition on vector transport and retraction that guarantees convergence and facilitates efficient computation is derived. Experimental evidence is presented demonstrating the value of the extension to the Riemannian Broyden class through superior performance for some problems compared to existing Riemannian BFGS methods, in particular those that depend on differentiated retraction.

/pdf/a-broyden-class-of-quasi-newton-methods-for-riemannian-im5919lphn.pdf

A Broyden Class of Quasi-Newton Methods for Riemannian Optimization

The well-known symmetric rank-one trust-region method--where the Hessian approximation is generated by the symmetric rank-one update--is generalized to the problem of minimizing a real-valued function over a $$d$$ d -dimensional Riemannian manifold. The generalization relies on basic differential-geometric concepts, such as tangent spaces, Riemannian metrics, and the Riemannian gradient, as well as on the more recent notions of (first-order) retraction and vector transport. The new method, called RTR-SR1, is shown to converge globally and $$d+1$$ d + 1 -step q-superlinearly to stationary points of the objective function. A limited-memory version, referred to as LRTR-SR1, is also introduced. In this context, novel efficient strategies are presented to construct a vector transport on a submanifold of a Euclidean space. Numerical experiments--Rayleigh quotient minimization on the sphere and a joint diagonalization problem on the Stiefel manifold--illustrate the value of the new methods.

http://www.optimization-online.org/DB_FILE/2013/06/3905.pdf

A Riemannian symmetric rank-one trust-region method

This dissertation generalizes three well-known unconstrained optimization approaches for Rn to solve optimization problems with constraints that can be viewed as a d-dimensional Riemannian manifold to obtain the Riemannian Broyden family of methods, the Riemannian symmetric rankone trust region method, and Riemannian gradient sampling method. The generalization relies on basic differential geometric concepts, such as tangent spaces, Riemannian metrics, and the Riemannian gradient, as well as on the more recent notions of (first-order) retraction and vector transport. The effectiveness of the methods and techniques for their efficient implementation are derived and evaluated. Basic experiments and applications are used to illustrate the value of the proposed methods. Both the Riemannian symmetric rank-one trust region method and the RBroyden family of methods are generalized from Euclidean quasi-Newton optimization methods, in which a Hessian approximation exploits the well-known secant condition. The generalization of the secant condition and the associated update formulas that define quasi-Newton methods to the Riemannian setting is a key result of this dissertation. The dissertation also contains convergence theory for these methods. The Riemannian symmetric rank-one trust region method is shown to converge globally to a stationary point and d+1-step q-superlinearly to a minimizer of the objective function. The RBroyden family of methods is shown to converge globally and q-superlinearly to a minimizer of a retraction-convex objective function. A condition, called the locking condition, on vector transport and retraction that guarantees convergence for the RBroyden family method and facilitates efficient computation is derived and analyzed. The Dennis Moré sufficient and necessary conditions for superlinear convergence, can be generalized to the Riemannian setting in multiple ways. This dissertation generalizes them in a novel manner that is applicable to both Riemannian optimization problems and root finding for a vector field on a Riemannian manifold. The convergence analyses of Riemannian symmetric rank-one trust region method and RBroyden family methods assume a smooth objective function. For partly smooth Lipschitz continuous objective functions, a variation of one of the RBroyden family methods, RBFGS, is shown to be work well empirically. In addition, the Riemannian gradient sampling method is shown to work

/pdf/optimization-algorithms-on-riemannian-manifolds-with-4hgoad8don.pdf

Optimization Algorithms on Riemannian Manifolds with Applications

This paper presents line search algorithms for finding extrema of locally Lipschitz functions defined on Riemannian manifolds. To this end we generalize the so-called Wolfe conditions for nonsmooth functions on Riemannian manifolds. Using $\varepsilon$-subgradient-oriented descent directions and the Wolfe conditions, we propose a nonsmooth Riemannian line search algorithm and establish the convergence of our algorithm to a stationary point. Moreover, we extend the classical BFGS algorithm to nonsmooth functions on Riemannian manifolds. Numerical experiments illustrate the effectiveness and efficiency of the proposed algorithm.

Line Search Algorithms for Locally Lipschitz Functions on Riemannian Manifolds

In the Euclidean setting the proximal gradient method and its accelerated variants are a class of efficient algorithms for optimization problems with decomposable objective. In this paper, we develop a Riemannian proximal gradient method (RPG) and its accelerated variant (ARPG) for similar problems but constrained on a manifold. The global convergence of RPG is established under mild assumptions, and the O(1/k) is also derived for RPG based on the notion of retraction convexity. If assuming the objective function obeys the Rimannian Kurdyka–Łojasiewicz (KL) property, it is further shown that the sequence generated by RPG converges to a single stationary point. As in the Euclidean setting, local convergence rate can be established if the objective function satisfies the Riemannian KL property with an exponent. Moreover, we show that the restriction of a semialgebraic function onto the Stiefel manifold satisfies the Riemannian KL property, which covers for example the well-known sparse PCA problem. Numerical experiments on random and synthetic data are conducted to test the performance of the proposed RPG and ARPG.

Wen Huang

Papers

A Broyden Class of Quasi-Newton Methods for Riemannian Optimization

A Riemannian symmetric rank-one trust-region method

Optimization Algorithms on Riemannian Manifolds with Applications

Line Search Algorithms for Locally Lipschitz Functions on Riemannian Manifolds

Riemannian proximal gradient methods