Spearheaded a comprehensive project on "Optimization Techniques in Convex Functions," delving into root-finding algorithms, Gradient Descent, and Linear Programming. Developed and implemented ...
Abstract: This article presents a prediction-correction proximal method (PCPM) for the general nonsmooth convex optimization problem with linear equality and inequality constraints. The proposed ...
Abstract: The control capability of boost-glide vehicle models controlled by the angle of attack and the sideslip angle is weak. Such vehicles' ascent trajectory optimization exhibits characteristics ...
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 ...
Convex optimisation constitutes a fundamental area in applied mathematics where the objective is to identify the minimum of a convex function subject to a set of convex constraints. This framework ...
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 ...
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, ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback