#### Filter Results:

- Full text PDF available (177)

#### Publication Year

1958

2017

- This year (2)
- Last 5 years (24)
- Last 10 years (125)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Emmanuel J. Candès, Terence Tao
- IEEE Transactions on Information Theory
- 2005

This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f/spl isin/R/sup n/ from corrupted measurements y=Af+e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible to recover f exactly from the data y? We prove that under suitable conditions… (More)

- Emmanuel J. Candès, Justin K. Romberg, Terence Tao
- IEEE Transactions on Information Theory
- 2006

This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f/spl isin/C/sup N/ and a randomly chosen set of frequencies /spl Omega/. Is it possible to reconstruct f from the partial knowledge of its Fourier coefficients on the set /spl Omega/? A typical result of this paper is as… (More)

Suppose we wish to recover a vector x 0 ∈ R m (e.g. a digital signal or image) from incomplete and contaminated observations y = Ax 0 + e; A is a n by m matrix with far fewer rows than columns (n m) and e is an error term. Is it possible to recover x 0 accurately based on the data y? To recover x 0 , we consider the solution x to the 1-regularization… (More)

- Terence Tao
- 2005

In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n p, and the z i 's are i.i.d. N (0, σ 2). Is it possible to… (More)

- Emmanuel J. Candès, Terence Tao
- IEEE Transactions on Information Theory
- 2006

Suppose we are given a vector f in a class FsubeRopf<sup>N </sup>, e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to recover f to within precision epsi in the Euclidean (lscr<sub>2</sub>) metric? This paper shows that if the objects of interest are sparse in a fixed basis or… (More)

- Markus Keel, Terence Tao
- 2007

We prove an abstract Strichartz estimate, which implies previously unknown endpoint Strichartz estimates for the wave equation (in dimension n 4) and the Schrr odinger equation (in dimension n 3). Three other applications are discussed: local existence for a nonlinear wave equation; and Strichartz-type estimates for more general dispersive equations and for… (More)

- Emmanuel J. Candès, Terence Tao
- IEEE Transactions on Information Theory
- 2010

This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the <i>matrix completion</i> problem, and comes up in a great number of applications, including the famous <i>Netflix Prize</i> and other similar questions in collaborative filtering. In general, accurate recovery of a matrix from… (More)

- Terence Tao
- 2008

We prove that there are arbitrarily long arithmetic progressions of primes. There are three major ingredients. The first is Szemerédi's theorem, which asserts that any subset of the integers of positive density contains progressions of arbitrary length. The second, which is the main new ingredient of this paper, is a certain transference principle. This… (More)

- Terence Tao
- 2005

Contents Preface ix Chapter 1. Ordinary differential equations 1 1.1. General theory 1 1.2. Gronwall's inequality 7 1.3. Bootstrap and continuity arguments 14 1.4. Noether's theorem 18 1.5. Monotonicity formulae 25 1.6. Linear and semilinear equations 28 1.7. Completely integrable systems 35 Chapter 2. Constant coefficient linear dispersive equations 41… (More)

- TERENCE TAO
- 2005

Let G be a finite abelian group, and let f : G → C be a complex function on G. The uncertainty principle asserts that the support supp(f) := {x ∈ G : f (x) = 0} is related to the support of the Fourier transformˆf : G → C by the formula |supp(f)||supp(ˆ f)| ≥ |G| where |X| denotes the cardinality of X. In this note we show that when G is the cyclic group… (More)