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Found 9 papers, 3 papers with code
Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization
We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum annealers (QA).
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms.
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity
This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form.
Stochastic Frank-Wolfe for Composite Convex Minimization
A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints.
Online Adaptive Methods, Universality and Acceleration
We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard convergence rate in the stochastic optimization setting.
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of linear updates.
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
This paper concerns a fundamental class of convex matrix optimization problems.
Practical sketching algorithms for low-rank matrix approximation
This paper describes a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a sketch.
A Universal Primal-Dual Convex Optimization Framework
We propose a new primal-dual algorithmic framework for a prototypical constrained convex optimization template.
