Spearheaded a comprehensive project on "Optimization Techniques in Convex Functions," delving into root-finding algorithms, Gradient Descent, and Linear Programming. Developed and implemented ...

In this assignment, written in Matlab code, we implement specific algorithms from Optimization Techniques on convex functions of one or multiple variables, aiming to find their minimum point. In the ...

Convex optimization algorithms such as gradient descent, quasi-Newton methods, and their variants are designed to find the global minimum of strongly convex functions. When these algorithms are ...

Let C and D be n × q matrices, whose columns are real-valued functions on the vertices of the graphs A and ... Evaluation of convex optimization techniques for the weighted graph-matching problem in ...

Each agent in the network has its own convex cost function. We consider a gradient-based distributed method and use a push-pull gradient algorithm to minimize the total cost function. Inspired by the ...

1. Introduction. Optimization is not merely a tool in the arsenal of machine learning and artificial intelligence; it is the very foundation upon which the efficacy and efficiency of these domains ...

[3] An Optimization Methodology for Designing Hardware-Based Function Evaluation Modules with Reduced Complexity. Circuits, Systems, and Signal Processing (2021). Back to "Electronic Instrumentation" ...