Found 1758 results, showing the newest relevant preprints. Sort by relevancy only.Update me on new preprints

Single Versus Union: Non-parallel Support Vector Machine Frameworks

It solves a series of small optimization problems to obtain a series of hyperplanes, but is hard to measure the loss of each sample. Expand abstract.
95 days ago
7/10 relevant
arXiv

Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine

The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments. Expand abstract.
97 days ago
10/10 relevant
arXiv

S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization

The intention of this work is to solve large-scale optimization problems where the local objective function is complicated and numerous. Expand abstract.
97 days ago
5/10 relevant
arXiv

A solution for fractional PDE constrained optimization problems using reduced basis method

In this paper, we employ a reduced basis method for solving the PDE constrained optimization problem governed by a fractional parabolic equation with the fractional derivative in time from order beta in (0,1) is defined by Caputo fractional derivative. Expand abstract.
98 days ago
9/10 relevant
arXiv

Optimization Hierarchy for Fair Statistical Decision Problems

We use this insight to construct an optimization hierarchy that lends itself to numerical computation, and we use tools from variational analysis and random set theory to prove that higher levels of this hierarchy lead to consistency in the sense that it asymptotically imposes this independence as a constraint in corresponding statistical... Expand abstract.
98 days ago
4/10 relevant
arXiv

A Saddle-Point Dynamical System Approach for Robust Deep Learning

Although such training involves highly non-convex non-concave robust optimization problems, empirical results show that the algorithm can achieve significant robustness for deep learning. Expand abstract.
98 days ago
7/10 relevant
arXiv

The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size

The Quantum Approximate Optimization Algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization problems whose performance can only improve with the number of layers $p$. Expand abstract.
99 days ago
4/10 relevant
arXiv

Semiclassical optimization of entrainment stability and phase coherence in weakly forced quantum nonlinear oscillators

By using the semiclassical phase reduction theory recently developed for quantum nonlinear oscillators, two types of optimization problems, one for the stability and the other for the phase coherence of the entrained state, are considered. Expand abstract.
100 days ago
4/10 relevant
arXiv

Constrained Bayesian Optimization with Max-Value Entropy Search

On an extensive set of real-world constrained hyperparameter optimization problems we show that cMES compares favourably to prior work, while being simpler to implement and faster than other constrained extensions of Entropy Search. Expand abstract.
101 days ago
4/10 relevant
arXiv

ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

The adaptive momentum method (AdaMM), which uses past gradients to update descent directions and learning rates simultaneously, has become one of the most popular first-order optimization methods for solving machine learning problems. Expand abstract.
102 days ago
4/10 relevant
arXiv