While the use of statistical methods to identify financial risk factors is a long-standing practice, the use of convex optimization for this purpose is a recent innovation. Specifically, a combination of convex programs

Source URL: mmds-data.org

CS168: The Modern Algorithmic Toolbox Lecture #6: Stochastic Gradient Descent and Regularization Tim Roughgarden & Gregory Valiant∗ April 13, 2016

Source URL: theory.stanford.edu

• Statistics
• Numerical analysis
• Mathematics
• Computational statistics
• Regression analysis
• Econometrics
• Convex optimization
• Estimation theory
• Stochastic gradient descent
• Gradient descent
• Linear regression
• Mathematical optimization

JMLR: Workshop and Conference Proceedings vol 40:1–46, 2015 Escaping From Saddle Points – Online Stochastic Gradient for Tensor Decomposition Rong Ge

Source URL: jmlr.org

• Mathematical analysis
• Numerical analysis
• Mathematical optimization
• Multivariable calculus
• Convex analysis
• Stochastic optimization
• Convex optimization
• Stochastic gradient descent
• Hessian matrix
• Gradient descent
• Lagrange multiplier
• Higher-order singular value decomposition

Region of Attraction Estimation for a Perching Aircraft: A Lyapunov Method Exploiting Barrier Certificates Elena Glassman, Alexis Lussier Desbiens, Mark Tobenkin, Mark Cutkosky, and Russ Tedrake Abstract— Dynamic perch

Source URL: eglassman.github.io

• Polynomials
• Mathematics
• Mathematical analysis
• Algebra
• Polynomial
• Optimization problem
• Convex optimization
• Mathematical optimization
• Degree of a polynomial
• Numerical analysis

Dual Decomposition with Many Overlapping Components Andr´e F. T. Martins∗† Noah A. Smith∗ Pedro M. Q. Aguiar‡ M´ario A. T. Figueiredo† ∗ School of Computer Science, Carnegie Mellon University, Pittsburgh, P

Source URL: users.isr.ist.utl.pt

• Mathematical optimization
• Operations research
• Mathematics
• Mathematical analysis
• Convex optimization
• Subgradient method
• Augmented Lagrangian method
• Decomposition method
• Dynamic programming
• Algorithm
• Linear programming

CS261: A Second Course in Algorithms Lecture #8: Linear Programming Duality (Part 1)∗ Tim Roughgarden† January 28,

Source URL: theory.stanford.edu

• Mathematical optimization
• Operations research
• Convex optimization
• Linear programming
• Mathematical analysis
• Duality
• Constraint
• Feasible region
• Semidefinite programming
• Lagrangian relaxation

Optimization in Image Processing Workshop Program Monday, June 27 9:30-10:20 Joachim Weickert, Saarland University Title: “FSI Schemes: Fast Semi-Iterative Methods for Diffusive or Variational Image

Source URL: cmsa.fas.harvard.edu

• Image processing
• Signal processing
• Mathematical optimization
• Operations research
• Electrical engineering
• Convex analysis
• Mathematics
• Mathematical analysis
• Deconvolution
• Iterative reconstruction
• Convex optimization
• Inverse problem

51 Documenta Math. Linear Programming Stories

Source URL: www.math.uiuc.edu

• Mathematics
• Mathematical optimization
• Operations research
• Geometry
• Convex optimization
• Lattice points
• Computational number theory
• Linear programming
• Ellipsoid method
• Leonid Khachiyan
• Lattice reduction
• LenstraLenstraLovsz lattice basis reduction algorithm

CS261: A Second Course in Algorithms Lecture #7: Linear Programming: Introduction and Applications∗ Tim Roughgarden† January 26, 2016

Source URL: theory.stanford.edu

• Mathematical optimization
• Operations research
• Numerical analysis
• Mathematical analysis
• Linear programming
• Convex optimization
• Combinatorial optimization
• Feasible region
• Duality
• Linear regression
• Candidate solution
• Linear-fractional programming

The last few years has seen a flurry of activity in non-convex approaches to enable solution of large scale optimization problems that come up in machine learning. The common thread in many of these results is that low-r

Source URL: mmds-data.org