Cbc VS pulp

Taking a look at pulp it just seems to be a solver abstraction API and all the real work is done by solver libraries written in other languages like C++. It looks like the default solver is COIN-OR CLP/CBC, and it looks like that's written in C++: https://github.com/coin-or/Cbc/tree/master/src

Maybe I'm misunderstanding something here and it's the abstraction API causing the problems, but it seems like it's up to the solver implementation to be efficient here?

Hi! I am searching for people who have previously used COIN-OR CBC through it's C++ libraries. I am specifically going to use their lotsize variable type ( Need for Creation of a new type of variable in the CBC Solver (Bucket Variable) · Discussion #516 · coin-or/Cbc (github.com) )

To use CBC (https://github.com/coin-or/Cbc) we need to compile it. Get source code from following address instead of github, this archive include all dependencies as well which will be compiled as well (if missing)

I've looked at it a little bit, but as someone nearing retirement, it is lacking a number of features that I would like.

There are some common tricks that I don't see how to model with ProjectionLab:

1. Do your tax-deferred 401k,403b,IRA saving in a high earning job, state, then move to a low or no income tax state to do the withdrawals in retirement.

2. Retire early so there is some time before taking social security payments to do Roth Conversions. I-ORP[0] turns this into a branch and cut linear optimization problem. User Indyhou at Bogleheads[1] has built a spreadsheet that uses a solver plugin. It may be possible to build a model in CBC[2] and compile it to WASM and run it in the browser.

3. After turning 63, watch the Roth Conversions to make sure you don't trigger IRRMA medicare surcharges.

4. Are you trying to stay under income limits for ACA subsidies? It's not quite the sharp cliff that it was, but can be important for some.

5. Are you trying to balance regular income and capital gains to take advantage of the 0% cap gains rates? You've got to plan ahead on your contributions to the taxable and tax deferred accounts for this to work. Jeremy at Go Curry Cracker has written about using this to pay $0 in US Federal Income taxes[3].

6. Paying full rate for health insurance will likely get you over the 7.5% limit for tax deductions.

7. Social Security claiming strategies can be complex for married couples.

A feature that would be useful during accumulation is life insurance planning for the death of a spouse.

The death of a spouse can throw a wrench in some of the strategies since the single tax bracket is much smaller. Tax law changes can also upset highly optimized strategies. So any highly optimized strategy should also have a monte carlo simulation around a spouse dying and tax law changes to understand what disruptions are possible and maybe accept a non-optimal strategy that is better in these adverse cases.

[0] https://i-orp.com

[1] https://www.bogleheads.org/forum/viewtopic.php?t=365518

[2] https://github.com/coin-or/Cbc

[3] https://www.gocurrycracker.com/go-curry-cracker-2020-taxes/

Optimization with PuLP

Now I have used Pulp library, which I guess does the heavy lifting.

Pyomo, it even has its own book. Additionally, CVXOPT focuses on convex optimization, PuLP on linear programming (it has lots of interfaces for other solvers).

After installing https://github.com/coin-or/pulp via pip I failed to use the built-in cbc solver. Pulp comes with prebuilt binaries for Linux x32 & x64 but not for ARM, so it threw error while solving problem.

Python has few libraries, one that I used it is PULP but there are others and as mentioned above, MS Excel has the Solver plugin to do it. Hope is hepful.

Hi, you should use an optimization linear programming package, also MS excel. With solver, if you use Python try to read the Documentaton of PULP library here is the link PULP Library Python hope it helps

There are some optimization modeling tools, Pulp andScipy are known for linear programming (LP) modeling, CVXOPT and Pyomo for quadratic programming (QP).