Abstract: This article proposes a novel low-complexity syndrome-based linear programming (SB-LP) decoding algorithm for decoding quantum low-density parity-check codes. Under the code-capacity model, ...

MPAX is a hardware-accelerated, differentiable, batchable, and distributable solver for mathematical programming in JAX, designed to integrate with modern computational and deep learning workflows: ...

Learn the Adagrad optimization algorithm, how it works, and how to implement it from scratch in Python for machine learning models. #Adagrad #Optimization #Python Blue Jays' John Schneider delivers ...

OpenAI and Google DeepMind demonstrated that their foundation models could outperform human coders — and win — showing that large language models (LLMs) can solve complex, previously unsolved ...

Note that the optimal solution to Gonzaga’s problem denoted by (G) is [a, 0] T with an optimal value of the objective function equal to a, a ≥ 10. From the infeasible starting point e = [1, 1] T, the ...

Abstract: In this article, the data-driven optimal control problem is addressed for discrete-time linear periodic systems with unknown system dynamics. To reduce the number of iterations required by ...

This project solves a toy distribution optimization problem using linear programming. The goal is to maximize the number of toys distributed to children while respecting constraints related to factory ...

ABSTRACT: The alternating direction method of multipliers (ADMM) and its symmetric version are efficient for minimizing two-block separable problems with linear constraints. However, both ADMM and ...

Many important practical computations, such as scheduling, combinatorial, and optimization problems, use techniques known as integer programming to find the best combination of many variables. In ...