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# 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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### Intuitive motivation for BFGS update

I am teaching a numerical analysis survey class and am seeking motivation for the BFGS method for students with limited background/intuition in optimization! While I don't have time to prove ...
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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 ...
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### 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 ...
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### Solving a least squares problem with linear constraints in Python

I need to solve \begin{alignat}{1} & \min_{x}\|Ax - b\|^2_{2}, \\ \mathrm{s.t.} & \quad\sum_{i}x_{i} = 1, \\ & \quad x_{i} \geq 0, \quad \forall{i}. \end{alignat} I think it is a ...
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### LP feasibility checking

I have a linear programming problem. I want to know if this LP is feasible. What is the best known algorithm for checking feasibility of an LP or a linear system of equations?
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### Find constrained vectors maximizing angles between them - methods?

This is related to a question I had asked earlier, with the distinction that earlier I did not have a non-linear objective functional to minimize. The problem is reproduced below with added ...
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### Is there an optimization scheme/algorithm that converges, for this non-convex scenario but with some special properties

I have a smooth function $f(x) = \frac{g(x)}{h(x)}$ that is the ratio of two smooth convex functions $g(x)$ and $h(x)$. It is known that $f(x)$ has a global minimum, achieved at the unique point $x_0$....
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### Formulation of the least-squares parameter estimation problem

I have a system of 10 ordinary differential equations of the form, $$\frac{dy_1}{dt} = f1(V1,k1,y1,y2)\\ \vdots \\ \frac{dy_{10}}{dt} = f_{10}(V_{10},k_{10},y_{9},y_{10})$$ I want to estimate the ...
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### Simultaneous maximization of two functions without available derivatives

I have two variables k and t as functions of two other variables p1 and ...
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### minimization problem: sum of Rayleigh quotients

I would like to find $x$ which minimizes the following equation: $\frac{x^HAx}{x^HBx} + \frac{x^HCx}{x^HDx}$ where A, B, C, D are positive-definite. $x$ is not a very large vector (<1000 elements ...
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### Maximization variant of semidefinite programming (SDP)

Consider the following program: $$\max_{\pmb a} \sum_i z_i\\ u.c. \pmb a \pmb P_i\pmb a^\top\geq z_i$$ where $\pmb a \in\mathbb{R}^p$ and the $\pmb P_i$ are all symmetric positive semidefinite ...
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In these notes (section 2.3), it is stated that: A point $x^*$ is a minimizer of a function $f$ (not necessarily convex) if and only if $f$ is subdifferentiable at $x^*$ and $0 \in\partial f(x^*).$ ...
Consider the following optimization problem: $Min \qquad C^TX$ S.t.: $\qquad AX=0;$ $x_ix_j=x_kx_t$$\quad$for some $i\neq j\neq k\neq t$ $X=(x_1,x_2,...x_n)$ and \$\quad x_j\geq 0\;\; j=1,2,......