BEST APPROXIMATIONS IN L1 ARE NEAR BEST IN Lp , p < 1 LAWRENCE G. BROWN AND BRADLEY J. LUCIER (Communicated by J. Marshall Ash) Abstract. We show that any best L1 polynomial approximation to a function f in Lp , 0 < p <

Source URL: www.math.purdue.edu

• Differentiation rules
• Functions and mappings
• Ordinary differential equations
• Mathematical identities
• Integral calculus
• Product rule
• SturmLiouville theory

Capricorn: An Algorithm for Subtropical Matrix Factorization Sanjar Karaev∗ Abstract Max-times algebra, sometimes known as subtropical algebra, is a semi-ring over the nonnegative real numbers where the addition operat

Source URL: people.mpi-inf.mpg.de

• Matrix theory
• Linear algebra
• Numerical linear algebra
• Multivariate statistics
• Non-negative matrix factorization
• Matrix
• Singular value decomposition
• Rank
• Block matrix
• Low-rank approximation

c 2012 Society for Industrial and Applied Mathematics  SIAM J. OPTIM. Vol. 22, No. 4, pp. 1549–1578

Source URL: stanford.edu

• Generalized functions
• Stochastic optimization
• Mathematical optimization
• Ergodic theory
• Mixing
• Stochastic approximation
• Markov chain
• Distribution
• Subgradient method
• Central limit theorem

A Quadratically Convergent Algorithm for Structured Low-Rank Approximation ´ Eric Schost1 and Pierre-Jean Spaenlehauer2 1

Source URL: www.csd.uwo.ca

• Numerical linear algebra
• Linear algebra
• Matrix theory
• Functional analysis
• Matrices
• Low-rank approximation
• Singular value decomposition
• Matrix
• MoorePenrose pseudoinverse
• Kernel
• Projection
• Linear map

Journal of Machine Learning Research450 Submitted 11/07; Revised 11/09; Published 1/10 Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting

Source URL: www.cs.stevens.edu

• Multivariate statistics
• Dimension reduction
• Computational statistics
• Machine learning
• Isomap
• Dimensionality reduction
• Nonlinear dimensionality reduction
• Semidefinite embedding
• Principal component analysis
• Tensor
• Manifold
• Multidimensional scaling

MONAURAL SEPARATION AND CLASSIFICATION OF NON-LINEAR TRANSFORMED INDEPENDENT SIGNALS: AN SVM PERSPECTIVE Sepp Hochreiter and Michael C. Mozer Department of Computer Science University of Colorado Boulder, CO 80309

Source URL: www.bioinf.jku.at

• Signal processing
• Statistical models
• Convex optimization
• Operations research
• Independent component analysis
• Wavelet
• Linear programming
• Support vector machine
• Matrix
• Machine learning
• Sparse approximation
• Sparse dictionary learning

218CHAPTER 8. GENERALIZATION AND FUNCTION APPROXIMATION Initialize θ! arbitrarily Repeat (for each episode): !e = !0 s, a ← initial state and action of episode

Source URL: webdocs.cs.ualberta.ca

DIRECTIVE 98/7/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 February 1998 amending DirectiveEEC for the approximation of the laws, regulations and administrative provisions of the Member States concerni

Source URL: www.fecma.eu

• Mathematical finance
• Interest rates
• Banking
• Annual percentage rate
• Personal finance
• Interest
• Decimal
• Significant figures

September 18, :5 WSPC - Proceedings Trim Size: 9.75in x 6.5in

Source URL: www.math.vt.edu

• Quantum mechanics
• Quantum chemistry
• Differential equations
• Approximations
• Mathematical physics
• BornOppenheimer approximation
• Partial differential equation
• Wave
• Perturbation theory
• LandauZener formula
• WKB approximation
• Diabatic

1 Tensor Completion for Estimating Missing Values in Visual Data Ji Liu, Przemyslaw Musialski, Peter Wonka, and Jieping Ye Abstract—In this paper we propose an algorithm to estimate missing values in tensors of visual

Source URL: peterwonka.net

• Linear algebra
• Matrix theory
• Tensors
• Mathematical optimization
• Numerical linear algebra
• Matrix completion
• Tensor rank decomposition
• Low-rank approximation
• Singular value decomposition
• Matrix
• Tensor
• Semidefinite programming