I want to solve a large scale non linear optimization problem and there are two methods interior point method and sequential quadric programing usually used to solve non linear optimization problem. I need suggestions regarding these methods which to use in term of implementation.
Sequential quadratic programming methods are mainly useful for problems with expensive evaluations. They can also be relevant in some cases if a good initial point is available.
On the other hand, interior point methods are suited for large-scale problems with cheaper evaluations.
Therefore, you can try an interior point method first. But do not hesitate to compare both on your problem. And unless you really want to implement them yourself, you should rather just write models for solvers that already implement them. Then, you can easily switch between different algorithms.
As @fontanf said, you're better off writing a mathematical model (AMPL, GAMS, JuMP, Pyomo, etc) and call an existing solver. There are extremely robust solvers out there (e.g. filterSQP and IPOPT), it would be a shame to reinvent the wheel.
Should you decide to implement your own solver anyway, you can check out the code of my modern C++ solver Uno (Unifying Nonlinear Optimization).