Results 81 to 90 of about 166 (143)

A method for convex black-box integer global optimization

We study the problem of minimizing a convex function on a nonempty, finite subset of the integer lattice when the function cannot be evaluated at noninteger points.
Stefan M. Wild, Jeffrey Larson, Prashant Palkar, Sven Leyffer   +3 more
core   +1 more source

Optimal Designs for Discrete Choice Models Via Graph Laplacians. [PDF]

J Stat Theory Pract
Röttger F, Kahle T, Schwabe R.
europepmc   +1 more source

Variable Smoothing for Convex Optimization Problems Using Stochastic Gradients. [PDF]

J Sci Comput, 2020
Boţ RI, Böhm A.
europepmc   +1 more source

Subgradient ellipsoid method for nonsmooth convex problems. [PDF]

Math Program, 2023
Rodomanov A, Nesterov Y.
europepmc   +1 more source

Matrix nearness problems with Bregman divergences

, 2008
. This paper discusses a new class of matrix nearness problems that measure approximation error using a directed distance measure called a Bregman divergence.
Inderjit S. Dhillon, A. Tropp, Joel
core  

A fast continuous time approach for non-smooth convex optimization using Tikhonov regularization technique. [PDF]

Comput Optim Appl
Karapetyants MA.
europepmc   +1 more source

On The Generic Properties Of Convex Optimization Problems In Conic Form

, 1997
We prove that strict complementarity, primal and dual nondegeneracy of optimal solutions of convex optimization problems in conic form are generic properties.
Levent Tunçel, Gábor Pataki
core  

A first order method for linear programming parameterized by circuit imbalance. [PDF]

Math Program
Cole R, Hertrich C, Tao Y, Végh LA.
europepmc   +1 more source

Self-Scaled Cones and Interior-Point Methods in Nonlinear Programming

, 1994
: This paper provides a theoretical foundation for efficient interior-point algorithms for nonlinear programming problems expressed in conic form, when the cone and its associated barrier are self-scaled.
M.J. Todd, Yu.E. Nesterov
core  

A Block Paige-Saunders Bidiagonalization Framework for Large-Scale Nuclear Norm Regularized Least Squares Problems

This paper proposes a framework for solving large-scale nuclear norm regularized least squares (NNRLS) problems based on the block Paige-Saunders bidiagonalization (PSB) process.
Bo Feng
core   +1 more source