# Questions tagged [optimization]

This tag is intended for questions on methods for the (constrained or unconstrained) minimization or maximization of functions.

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17answers
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### Is there a high quality nonlinear programming solver for Python?

I have several challenging non-convex global optimization problems to solve. Currently I use MATLAB's Optimization Toolbox (specifically, fmincon() with algorithm=<...
3answers
19k views

### BFGS vs. Conjugate Gradient Method

What considerations should I be making when choosing between BFGS and conjugate gradient for optimization? The function I am trying to fit with these variables are exponential functions; however, the ...
8answers
3k views

4answers
16k views

### What's the fastest software(open source) to solve mixed integer programming problem

I have a mixed integer programming problem. And I am current using GLPK as my solver. But I found that GLPK is good for Linear Programming problem, but for Mixed Integer programming, it requires much ...
3answers
6k views

### Fortran: Best way to time sections of your code?

Sometimes while optimizing code it is required to time certain portions of the code, I have been using the following for years but was wondering if there is a simpler/better way to do it? ...
2answers
2k views

### Newton-based methods in optimization vs. solving systems of nonlinear equations

I asked for clarification about a recent question about minpack, and got the following comment: Any system of equations is equivalent to an optimization problem, which is why Newton-based methods ...
1answer
3k views

### Pressure as a Lagrange Multiplier

In the incompressible Navier-Stokes equations, \begin{align*} \rho\left(\mathbf{u}_t + (\mathbf{u} \cdot \nabla)\mathbf{u}\right) &= - \nabla p + \mu\Delta\mathbf{u} + \mathbf{f}\\ \nabla\cdot\...
2answers
11k views

### Confusion about Armijo rule

I have this confusion about Armijo rule used in line search. I was reading back tracking line search but didn't get what this Armijo rule is all about. Can anyone elaborate what Armijo rule is? The ...
1answer
4k views

### Efficient solution of mixed integer linear programs

Many important problems can be expressed as a mixed integer linear program. Unfortunately computing the optimal solution to this class of problems is NP-Complete. Luckily there are approximation ...
1answer
6k views

2answers
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### How is geometric programming different from convex programming?

How is (generalized) geometric programming different from general convex programming? A geometric program can be transformed into a convex program, and is typically solved by an interior point method....
2answers
2k views

### Calculating Lagrange coefficients for SVM in Python

I'm trying to write a full SVM implementation in Python and I have a few issues computing the Lagrange coefficients. First let me rephrase what I understand from the algorithm to make sure I'm on the ...
2answers
674 views

### Trace An Isoline of an Expensive 2D Function

I have a problem similar in formulation to this post, with a few notable differences: What simple methods are there for adaptively sampling a 2D function? Like in that post: I have a $f(x,y)$ and ...
3answers
2k views

### Is providing approximate gradients to a gradient based optimizer useless?

Is it pointless to use gradient based optimization algorithms if you can only provide a numerical gradient? If not, why provide a numerical gradient in the first place if it is trivial to perform ...
5answers
955 views

### How can I automate the process of optimizing the design of a physical object?

I'm trying to optimize a flow distributor in a tank such that the velocity and temperature distribution across any cross-section is relatively uniform. There are many parameters I can adjust to the ...
1answer
1k views

### Adaptive gradient descent step size when you can't do a line search

I have an objective function $E$ dependent on a value $\phi(x, t = 1.0)$, where $\phi(x, t)$ is the solution to a PDE. I am optimizing $E$ by gradient descent on the initial condition of the PDE: \$\...