Spearheaded a comprehensive project on "Optimization Techniques in Convex Functions," delving into root-finding algorithms, Gradient Descent, and Linear Programming. Developed and implemented ...
Abstract: The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates. Most previous studies in this field ...
Abstract: This article investigates a distributed time-varying optimization problem with inequality constraints, aiming to find finite-time and fixed-time convergent solutions free from initialization ...
We study competitive economy equilibrium computation. We show that, for the first time, the equilibrium sets of the following two markets: 1. A mixed Fisher and Arrow-Debreu market with homogeneous ...
ABSTRACT: In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order ...
The performance of optimization methods is often tied to the spectrum of the objective Hessian. Yet, conventional assumptions, such as smoothness, do often not enable us to make finely-grained ...
Numerical unconstrained optimization techniques (univariate search, Powell's method and Gradient Descent (fixed step and optimal step)) against these benchmark functions: De Jong’s function in 2D, ...
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