Efficient Constrained Optimization using ConicSolve.jl
Mathematical optimization is ubiquitous in scientific and engineering domains. We will explore how ConicSolve.jl, a Julia package is utilized to solve a variety of problem classes, including Linear (LP), Quadratic (QP), Second Order Cone (SOCP), and Semidefinite Programming (SDP). We'll cover examples in robotics, imaging and comms to discuss the techniques in modelling optimization problems and the design decisions made to make ConicSolve.jl a performant, versatile and extensible framework.