An introduction to optimization on smooth manifolds
Nicolas Boumal

Click here to download the book. Last update: June 24, 2022.

This version forms the basis for a forthcoming publication with Cambridge University Press. Please e-mail me about any mistakes you spot or suspect, be it typos or more serious things: I appreciate your input, and there may still be time to make the change before it goes to print. For general questions about geometry and optimization, please use the Manopt forum, MathOverflow or Math StackExchange, and feel free to let me know about it.

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About the book

This is a book about optimization on smooth manifolds for readers who are comfortable with linear algebra and multivariable calculus. There are no prerequisites in geometry or optimization. Chapters 3 and 5 in particular can serve as a standalone introduction to differential and Riemannian geometry. They focus on embedded submanifolds of linear spaces, with full proofs. A distinguishing feature is that these early chapters highlight computability and do not involve charts.

Chapter 8 provides the general theory so that we can build quotient manifolds in Chapter 9. The optimization algorithms in Chapters 4 and 6 apply to the general case, but can already be understood after reading Chapters 3 and 5. Chapter 7 details examples of submanifolds that come up in practice. Chapter 10 covers more advanced Riemannian tools, and Chapter 11 introduces geodesic convexity.

In a one-semester graduate course of the mathematics department at Princeton University in 2019 and 2020 (24 lectures of 80 minutes each, two projects, no exercises), I covered much of Chapters 1–6 and select parts of Chapter 7 before the midterm break, then much of Chapters 8–9 and select parts of Chapters 10–11 after the break. Those chapters were shorter at the time, but it still made for a sustained pace. At EPFL in 2021, I discussed mostly Chapters 1–8 in 13 lectures of 90 minutes, plus as many exercise sessions and two projects. The course is popular with applied mathematicians and mathematically inclined engineering students, at the graduate and advanced undergraduate level.

You may also be interested in the Manopt toolboxes (Matlab, Python, Julia), and in the book Optimization Algorithms on Matrix Manifolds by Absil, Mahony and Sepulchre (Princeton University Press, 2008), all freely available online.

These slides hold a summary of the basic geometric tools and algorithms from Chapters 3 and 5. Here are a one-hour video and a two-hour video introducing the basics of differential geometry and Riemannian geometry for optimization on smooth manifolds, using a variation of the slides linked here. These two videos have mostly the same contents.

How to cite this version

@Booklet{boumal2022intromanifolds,
  title        = {An introduction to optimization on smooth manifolds},
  author       = {Boumal, Nicolas},
  howpublished = {To appear with Cambridge University Press},
  month        = {Jun},
  year         = {2022},
  url          = {https://www.nicolasboumal.net/book},
}

Page numbers will change a lot between this version and the forthcoming published version. For now, it is best to reference section numbers as these should be stable.

Table of contents

  • Preface
  • 1. Introduction
  • 2. Simple examples
    • 2.1 Sensor network localization from directions: an affine subspace
    • 2.2 Single extreme eigenvalue or singular value: spheres
    • 2.3 Dictionary learning: products of spheres
    • 2.4 Principal component analysis: Stiefel and Grassmann
    • 2.5 Synchronization of rotations: special orthogonal group
    • 2.6 Low-rank matrix completion: fixed-rank manifold
    • 2.7 Gaussian mixture models: positive definite matrices
    • 2.8 Smooth semidefinite programs
  • 3. Embedded geometry: first order
    • 3.1 Reminders of Euclidean space
    • 3.2 Embedded submanifolds of a linear space
    • 3.3 Smooth maps on embedded submanifolds
    • 3.4 The differential of a smooth map
    • 3.5 Vector fields and the tangent bundle
    • 3.6 Moving on a manifold: retractions
    • 3.7 Riemannian manifolds and submanifolds
    • 3.8 Riemannian gradients
    • 3.9 Local frames*
    • 3.10 Notes and references
  • 4. First-order optimization algorithms
    • 4.1 A first-order Taylor expansion on curves
    • 4.2 First-order optimality conditions
    • 4.3 Riemannian gradient descent
    • 4.4 Regularity conditions and iteration complexity
    • 4.5 Backtracking line-search
    • 4.6 Local convergence*
    • 4.7 Computing gradients*
    • 4.8 Numerically checking a gradient*
    • 4.9 Notes and references
  • 5. Embedded geometry: second order
    • 5.1 The case for another derivative of vector fields
    • 5.2 Another look at differentials of vector fields in linear spaces
    • 5.3 Differentiating vector fields on manifolds: connections
    • 5.4 Riemannian connections
    • 5.5 Riemannian Hessians
    • 5.6 Connections as pointwise derivatives*
    • 5.7 Differentiating vector fields on curves
    • 5.8 Acceleration and geodesics
    • 5.9 A second-order Taylor expansion on curves
    • 5.10 Second-order retractions
    • 5.11 Special case: Riemannian submanifolds*
    • 5.12 Special case: metric projection retractions*
    • 5.13 Notes and references
  • 6. Second-order optimization algorithms
    • 6.1 Second-order optimality conditions
    • 6.2 Riemannian Newton's method
    • 6.3 Computing Newton steps: conjugate gradients
    • 6.4 Riemannian trust regions
    • 6.5 The trust-region subproblem: truncated CG
    • 6.6 Local convergence of RTR with tCG*
    • 6.7 Simplified assumptions for RTR with tCG*
    • 6.8 Numerically checking a Hessian*
    • 6.9 Notes and references
  • 7. Embedded submanifolds: examples
    • 7.1 Euclidean spaces as manifolds
    • 7.2 The unit sphere in a Euclidean space
    • 7.3 The Stiefel manifold: orthonormal matrices
    • 7.4 The orthogonal group and rotation matrices
    • 7.5 Fixed-rank matrices
    • 7.6 The hyperboloid model
    • 7.7 Manifolds defined by $h(x) = 0$
    • 7.8 Notes and references
  • 8. General manifolds
    • 8.1 A permissive definition
    • 8.2 The atlas topology, and a final definition
    • 8.3 Embedded submanifolds are manifolds
    • 8.4 Tangent vectors and tangent spaces
    • 8.5 Differentials of smooth maps
    • 8.6 Tangent bundles and vector fields
    • 8.7 Retractions and velocity of a curve
    • 8.8 Coordinate vector fields as local frames
    • 8.9 Riemannian metrics and gradients
    • 8.10 Lie brackets as vector fields
    • 8.11 Riemannian connections and Hessians
    • 8.12 Covariant derivatives and geodesics
    • 8.13 Taylor expansions and second-order retractions
    • 8.14 Submanifolds embedded in manifolds
    • 8.15 Notes and references
  • 9. Quotient manifolds
    • 9.1 A definition and a few facts
    • 9.2 Quotient manifolds through group actions
    • 9.3 Smooth maps to and from quotient manifolds
    • 9.4 Tangent, vertical and horizontal spaces
    • 9.5 Vector fields
    • 9.6 Retractions
    • 9.7 Riemannian quotient manifolds
    • 9.8 Gradients
    • 9.9 A word about Riemannian gradient descent
    • 9.10 Connections
    • 9.11 Hessians
    • 9.12 A word about Riemannian Newton's method
    • 9.13 Total space embedded in a linear space
    • 9.14 Horizontal curves and covariant derivatives
    • 9.15 Acceleration, geodesics and second-order retractions
    • 9.16 Grassmann manifold: summary*
    • 9.17 Notes and references
  • 10. Additional tools
    • 10.1 Distance, geodesics and completeness
    • 10.2 Exponential and logarithmic maps
    • 10.3 Parallel transport
    • 10.4 Lipschitz conditions and Taylor expansions
    • 10.5 Transporters
    • 10.6 Finite difference approximation of the Hessian
    • 10.7 Tensor fields and their covariant differentiation
    • 10.8 Notes and references
  • 11. Geodesic convexity
    • 11.1 Convex sets and functions in linear spaces
    • 11.2 Geodesically convex sets and functions
    • 11.3 Alternative definitions of geodesically convex sets*
    • 11.4 Differentiable geodesically convex functions
    • 11.5 Geodesic strong convexity and Lipschitz continuous gradients
    • 11.6 Example: positive reals and geometric programming
    • 11.7 Example: positive definite matrices
    • 11.8 Notes and references
  • Bibliography


Last update: June 24, 2022.