Recursos de colección
Caltech Authors (144.724 recursos)
Repository of works by Caltech published authors.
Group = Applied & Computational Mathematics
Repository of works by Caltech published authors.
Group = Applied & Computational Mathematics
Tropp, Joel A.; Yurtsever, Alp; Udell, Madeleine; Cevher, Volkan
This paper develops a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a sketch. These methods can preserve structural properties of the input matrix, such as positive-semidefiniteness, and they can produce approximations with a user-specified rank. The algorithms are simple, accurate, numerically stable, and provably correct. Moreover, each method is accompanied by an informative error bound that allows users to select parameters a priori to achieve a given approximation quality. These claims are supported by computer experiments.
Gittens, Alex A.; Tropp, Joel A.
This work introduces the minimax Laplace transform method, a modification of the cumulant-based matrix Laplace transform method developed in [Tro11c] that yields both
upper and lower bounds on each eigenvalue of a sum of random self-adjoint matrices. This machinery is used
to derive eigenvalue analogs of the classical Chernoff, Bennett, and Bernstein bounds.
Two examples demonstrate the efficacy of the minimax Laplace transform. The first concerns the effects of column sparsification on the spectrum of a matrix with orthonormal rows. Here, the behavior of the singular values can be described in terms of coherence-like quantities. The second
example addresses the question of relative accuracy...
Gittens, A.; Tropp, J. A.
Randomized matrix sparsification has proven to be a fruitful technique for producing
faster algorithms in applications ranging from graph partitioning to semidefinite programming. In
the decade or so of research into this technique, the focus has been—with few exceptions—on
ensuring the quality of approximation in the spectral and Frobenius norms. For certain graph
algorithms, however, the ∞→1 norm may be a more natural measure of performance.
This paper addresses the problem of approximating a real matrix A by a sparse random matrix
X with respect to several norms. It provides the first results on approximation error in the ∞→1
and ∞→2 norms, and it uses a result...
Chen, Richard Y.; Gittens, Alex A.; Tropp, Joel A.
Covariance estimation becomes challenging in the regime where the number p of variables outstrips the number n of samples available to construct the estimate. One way to circumvent
this problem is to assume that the covariance matrix is nearly sparse and to focus on estimating
only the significant entries. To analyze this approach, Levina and Vershynin (2011) introduce a
formalism called masked covariance estimation, where each entry of the sample covariance estimator
is reweighed to reflect an a priori assessment of its importance.
This paper provides a new analysis of the masked sample covariance estimator based on the
matrix Laplace transform method. The main result applies...
Tropp, Joel A.
This report presents probability inequalities for sums of adapted sequences of random,
self-adjoint matrices. The results frame simple, easily verifiable hypotheses on the summands, and
they yield strong conclusions about the large-deviation behavior of the maximum eigenvalue of the
sum. The methods also specialize to sums of independent random matrices.
Owhadi, Houman; Zhang, Lei
We construct finite-dimensional approximations of solution spaces of divergence
form operators with L^∞-coefficients. Our method does not rely on concepts of
ergodicity or scale-separation, but on the property that the solution of space of
these operators is compactly embedded in H^1 if source terms are in the unit ball
of L^2 instead of the unit ball of H^−1. Approximation spaces are generated by
solving elliptic PDEs on localized sub-domains with source terms corresponding to
approximation bases for H^2. The H^1-error estimates show that O(h^−d)-dimensional
spaces with basis elements localized to sub-domains of diameter O(h^∞ ln 1/h) (with α ∈ [1/2 , 1)) result in an O(h^(2−2α) accuracy...
Owhadi, H.; Scovel, C.; Sullivan, T. J.; McKerns, M.; Ortiz, M.
We propose a rigorous framework for Uncertainty Quantification (UQ) in which
the UQ objectives and the assumptions/information set are brought to the forefront.
This framework, which we call Optimal Uncertainty Quantification (OUQ), is based
on the observation that, given a set of assumptions and information about the problem,
there exist optimal bounds on uncertainties: these are obtained as extreme
values of well-defined optimization problems corresponding to extremizing probabilities
of failure, or of deviations, subject to the constraints imposed by the scenarios
compatible with the assumptions and information. In particular, this framework
does not implicitly impose inappropriate assumptions, nor does it repudiate relevant
information.
Although OUQ optimization problems are extremely large,...
Doostan, Alireza; Owhadi, Houman
We propose a method for the approximation of solutions of PDEs with stochastic coefficients
based on the direct non-adapted, i.e., non-adapted, sampling of solutions.
This sampling can be done by using any legacy code for the deterministic problem as
a black box. The method converges in probability (with probabilistic error bounds)
as a consequence of sparsity and a concentration of measure phenomenon on the
empirical correlation between samples. We show that the method is well suited for
truly high-dimensional problems (with slow decay in the spectrum).
Tropp, Joel A.
This work presents probability inequalities for sums of independent, random, self-adjoint
matrices. The results frame simple, easily verifiable hypotheses on the summands, and they
yield strong conclusions about the large-deviation behavior of the maximum eigenvalue of the sum.
Tail bounds for the norm of a sum of rectangular matrices follow as an immediate corollary, and
similar techniques yield information about matrix-valued martingales.
In other words, this paper provides noncommutative generalizations of the classical bounds
associated with the names Azuma, Bennett, Bernstein, Chernoff, Hoeffding, and McDiarmid. The
matrix inequalities promise the same ease of use, diversity of application, and strength of conclusion
that have made the scalar inequalities so...
Halko, N.; Martinsson, P. G.; Tropp, J. A.
Low-rank matrix approximations, such as the truncated singular value decomposition
and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing. This work surveys recent research which demonstrates that randomization offers a powerful
tool for performing low-rank matrix approximation. These techniques exploit modern computational
architectures more fully than classical methods and open the possibility of dealing with truly massive
data sets. In particular, these techniques o®er a route toward principal component analysis (PCA)
for petascale data.
This paper presents a modular framework for constructing randomized algorithms that compute
partial matrix decompositions. These methods use random sampling to identify a subspace that
captures most of...
Tao, Molei; Owhadi, Houman; Marsden, Jerrold E.
We introduce a new class of integrators for stiff ODEs as well as SDEs. An
example of subclass of systems that we treat are ODEs and SDEs that are sums of
two terms one of which has large coefficients. These integrators are (i) Multiscale:
they are based on
ow averaging and so do not resolve the fast variables but rather
employ step-sizes determined by slow variables (ii) Basis: the method is based on
averaging the
ow of the given dynamical system (which may have hidden slow and
fast processes) instead of averaging the instantaneous drift of assumed separated
slow and fast processes. This bypasses the need for...
Berlyand, Leonid; Owhadi, Houman
We consider linear divergence-form scalar elliptic equations and vectorial equations for elasticity with rough (L^∞(Ω), Ω ⊂ ℝ^d ) coefficients a(x) that, in particular,
model media with non-separated scales and high contrast in material properties.
While the homogenization of PDEs with periodic or ergodic coefficients and well
separated scales is now well understood, we consider here the most general case
of arbitrary bounded coefficients. For such problems we introduce explicit finite
dimensional approximations of solutions with controlled error estimates, which we
refer to as homogenization approximations. In particular, this approach allows one
to analyze a given medium directly without introducing the mathematical concept
of an ∈ family of...
Desbrun, Mathieu; Donaldson, Roger D.; Owhadi, Houman
We introduce a new geometric approach for the homogenization and
inverse homogenization of the divergence form elliptic operator with rough
conductivity coefficients σ(x) in dimension two. We show that conductivity coefficients are in one-to-one correspondence with divergence-free
matrices and convex functions s(x) over the domain Ω. Although homogenization is a non-linear and non-injective operator when applied directly
to conductivity coefficients, homogenization becomes a linear interpolation operator over triangulations of
Ω when re-expressed using convex
functions, and is a volume averaging operator when re-expressed with
divergence-free matrices. We explicitly give the transformations which
map conductivity coefficients into divergence-free matrices and convex
functions, as well as their respective inverses. Using...
Tropp, Joel A.; Wright, Stephen J.
In sparse approximation problems, the goal is to find
an approximate representation of a target signal using a linear
combination of a few elementary signals drawn from a fixed
collection. This paper surveys the major algorithms that are used
for solving sparse approximation problems in practice. Specific
attention is paid to computational issues, to the circumstances
in which individual methods tend to perform well, and to the
theoretical guarantees available. Many fundamental questions in
electrical engineering, statistics, and applied mathematics can
be posed as sparse approximation problems, which makes the
algorithms discussed in this paper versatile tools with a wealth
of applications.
Tropp, Joel A.
Given a fixed matrix, the problem of column subset selection requests a column submatrix that has favorable spectral properties. Most research from the algorithms and numerical linear
algebra communities focuses on a variant called rank-revealing QR, which seeks a well-conditioned
collection of columns that spans the (numerical) range of the matrix. The functional analysis literature contains another strand of work on column selection whose algorithmic implications have
not been explored. In particular, a celebrated result of Bourgain and Tzafriri demonstrates that
each matrix with normalized columns contains a large column submatrix that is exceptionally well
conditioned. Unfortunately, standard proofs of this result cannot be regarded...
Needell, D.; Tropp, J. A.
Compressive sampling offers a new paradigm for acquiring signals that are compressible
with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling
is to approximate a compressible signal from noisy samples. This paper describes a new iterative
recovery algorithm called CoSaMP that delivers the same guarantees as the best optimization-based
approaches. Moreover, this algorithm offers rigorous bounds on computational cost and storage.
It is likely to be extremely efficient for practical problems because it requires only matrix-vector
multiplies with the sampling matrix. For compressible signals, the running time is just O(N log^2 N),
where N is the length of the signal.
Tropp, Joel A.; Gilbert, Anna C.
This report demonstrates theoretically and empirically that a greedy algorithm called
Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension
d given O(mln d) random linear measurements of that signal. This is a massive improvement
over previous results, which require O(m2) measurements. The new results for OMP are comparable
with recent results for another approach called Basis Pursuit (BP). In some settings, the
OMP algorithm is faster and easier to implement, so it is an attractive alternative to BP for signal
recovery problems.