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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34 views

How to speed up the Mixed-Integer Quadratic Program process?

Currently, I am solving a problem in the format: M is an integer as well. The problem that troubles me is that X is a vector in {0,1} with a size of 7000. I use the solver in https://github.com/...
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0answers
35 views

implementation of shell elements in a topology optimization algorithm

I am working on developing a topology optimization solver based on the finite element method and I want to add a triangular shell element in it. I used the classic finite element method but I didn’t ...
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1answer
68 views

Solve two-player game - minimize the l-infinity norm of a matrix-vector product

I have a matrix $M$ with non-negative real entries, and I would like to minimize the objective function $$\Phi(v) = \|Mv\|_\infty,$$ where $v$ is constrained to be a probability vector, i.e., $v_1+\...
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2answers
229 views

Jacobians with automatic differentiation

I have an objective function F: Nx1 -> Nx1, where N>30000. There are many sparse matrix/tensor multiplications in this function, so taking an analytic Jacobian by paper and pen is cumbersome. ...
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0answers
30 views

What is the generalization of the resource allocation problem I'm dealing with here?

I'm dealing with a problem as follows: I have a finite set of money 𝑚 to spend over 𝑟 different raffles, and I need to spend approximately to my budget, with the goal of maximizing my probability ...
6
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1answer
334 views

When and when not to use automatic differentiation

I am just learning (more) about automatic differentiation (AD) and at this stage it kind of seems like black magic to me. The second paragraph of its Wikipedia article makes it sound too good to be ...
2
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0answers
31 views

Convergence of Truncated Newton for non-convex Hessian

I was wondering if anyone could enlighten me about the convergence properties of the truncated newton method in case of a non-positive definite hessian $\nabla^2 f = H$. From the Book 'Numerical ...
3
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2answers
82 views

Optimization of expensive model with many parameters

I have a physical model which takes $\sim50$ parameters and gives $\sim2000$ outputs taking tens of minutes to run. I need to optimize these parameters to give outputs as close as possible to data. ...
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0answers
18 views

Benchmark instances for directed 3-Cycle cover

The directed 3-Cycle cover asks for a vertex-covering set of oriented cycles with at least three vertices per cycle such that every vertex is covered by exactly one cycle. I have scrutinzed the ...
4
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1answer
130 views

How to define a dimensionless Objective function for determining how peaked a curve is?

I have attached 2 plots for FFT spectra. One is considered good and one is bad. The good one is classified on the basis of how closely spaced the frequencies and the bad is based on how multiple ...
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2answers
95 views

Using MILP to place a set of primers along a genome

Define variables $p_i,u_i\in\{0,1\}^G$, for $i=1,\ldots,8$ and $G=30000$. Let $v$ be a constant vector also in $\{0,1\}^G$, with approximately 25% of its entries equal to $1$ (randomly located). Let ...
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0answers
26 views

Estimating the dimension of a solution space in nonlinear least squares

Suppose I have a nonlinear least squares problem, $$ \min_{\mathbf{x}} || \mathbf{f}(\mathbf{x}) ||^2 $$ with $n$ residuals and $m$ parameters, so that $\mathbf{x} \in \mathbb{R}^m$, and $\mathbf{f} \...
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31 views

Non-negative Least Squares to perform Inverse Laplace with weights

I'm trying to perform the inverse Laplace transform of a (noisy) dataset $y_i$ using Tikhonov regularization: $$\min \sum_{i=1}^{N} \left(\int_0^\infty e^{-s_i t} f(t) \, dt - y_i \right)^2 - \lambda^...
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0answers
18 views

Optimization for sampling multiple points of maximized minimum distance

I'm trying to find a way to sample new points that have maximum minimum-distance (maximin distance). The current situation is where there are ns number of pre-existing sample points. I want N number ...
4
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2answers
7k views

Tikhonov regularization in the non-negative least square - NNLS (python:scipy)

I am working on a project that I need to add a regularization into the NNLS algorithm. Is there a way to add the Tikhonov regularization into the NNLS implementation of scipy [1]? [2] talks about it, ...
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1answer
303 views

Multi-objective optimization problem - Euclidean space

I am looking for some clues for an optimization problem. My problem consists of arriving to a image by optimizing multiple layers with the pixel position probability. This is an overview of the ...
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1answer
30 views

Norm constraint in CVXPY

I'm trying to implement the algorithm outlined in https://arxiv.org/abs/1211.5608 on a small scale. I have a linear operator $\mathcal{A}$ which is defined as $$\text{trace}(A^*_l(hm^*))$$ where $$A_l ...
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1answer
26 views

How to check curvature of a vector valued function

In terms of numerical optimization, the newton-rapson method requires a pos. definite Hessian $\nabla^2f$ respectively pos. curvature for computing the next step $p_k$ by solving $$\nabla^2 f p_k = -\...
1
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1answer
24 views

Genetic algorithm: fitness proportionate selection using RMSD as fitness function?

I'm implementing a genetic algorithm to optimise $x$ so as to minimise the RMSD error $r(x)$ between my model and experimental data. During the selection stage of recombination, I wish to select '...
1
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1answer
47 views

Making Hessian positive semidefinite

I have a large problem that I'm optimizing with Newton method. This involves a large sparse Hessian matrix. For better convergence and not to get stuck prematurely, I'd like to make the Hessian ...
2
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1answer
35 views

Plotting optimum as a function of parameter in the objective

I am trying to minimize a 2d function using scipy.optimize. Specifically I want to plot the minimum value of the function fun as a function of the parameter wjk. The problem is that I cannot pass wjk ...
1
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1answer
87 views

Machine Learning for Optimization

I have a function which takes 100+ coefficients and outputs $x$. I wish to optimise $x$. Running the simulation 50 000 times will take around 15 minutes, however, this happens in parallel - and the ...
1
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1answer
39 views

How do I get scipy.minimize to terminate below a certain loss threshold?

I was looking at the scipy.minimize documentation to see if I could find a way to terminate optimization when the loss gets below some cut-off, and I couldn't see ...
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1answer
61 views

R function or package for carrying out maximum likelihood techniques in random effect models

I am applying optim() function in R to obtain maximum likelihood estimates of the fixed effects and random effects in a model with bivariate random effects. The ...
2
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1answer
81 views

A concave maximization that is not supported on CVX

I try to solve a maximization problem using CVX. In its simplest form, I want to maximize $$f(x,y)=y*h_b\left(\frac{x}{y}\right),$$ where $h_b(\cdot)$ is the binary entropy function. In the context ...
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1answer
20 views

Python: Getting second output variable from minimizing a computationally intensive function on first outputs

I have a function in python that is quite computationally expensive to evaluate, of the form: ...
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2answers
71 views

How to minimize $(x-a)^2+(y-b)^2$ subject to $ \sqrt{a}+\sqrt{b}=\sqrt{2}$?

I am not sure if this is on-topic here, but I am trying. Let $x,y$ be positive real numbers. I am trying to find $$ \min_{\sqrt{a}+\sqrt{b}=\sqrt{2}}(x-a)^2+(y-b)^2$$ I tried using Mathematica for ...
4
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1answer
129 views

An optimization method for bounding the eigenvalues of a unknown non symmetric matrix

Given a positive objective function $f$ that acts on a real-valued matrix $A$, I am interested in the following problem $$\underset{A \in \mathbb{R}^{n \times n}}{\text{minimize}} \quad f(A) \quad \...
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1answer
29 views

Avalability of SNOPT optimization solver

I'd like to know if SNOPT solver is available free of cost for academic research in any of the optimization software packages. I came across a few softwares that have SNOPT, but those require a ...
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0answers
22 views

Can Scipy.optimize take a user-defined objective function that contains an ML model?

I have an optimization task that requires me to choose the optimal combinations of parameters, according to the prediction of a random forest model. My main obstacle is that scipy.optimize always ...
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0answers
18 views

Convex performance measure of classification

In the context of binary classifcation methods, I am looking for a performance metric that can be optimized in MATLAB. Since the data is not balanced, a good choice seems to be the so-called F1-...
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0answers
38 views

Optimizing vectors with equal elements

I am trying to distribute power across different devices, so that the sum is as equal as possible to the power setpoint. At the same time, the sum of power per phase must not exceed the power of the ...
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2answers
7k views

Simultaneous maximization of two functions without available derivatives

I have two variables k and t as functions of two other variables p1 and ...
1
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1answer
116 views

Some proof that linear translations and rotations of a bound-constrained function are equivalent

For example, I have a function to optimize: $$f_1(x,y) = x^2+y^2, \quad x_{lb}\le x\le x_{ub},\quad y_{lb}\le y\le y_{ub}$$ Then I apply rotation by $\theta$ plus translation by $x_0$ and $y_0$: $$f_2(...
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0answers
52 views

Does BFGS preserve the bandedness of the inverse hessian?

In the BFGS method we perform iterations by calculating an approximation $\boldsymbol{H}_k$ to the inverse Hessian $\boldsymbol{H}$ of the objective function. This method belongs to a family of ...
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1answer
46 views

Detecting degenerate triangles with very thin structures

Between the two ears in the following bunny images, there are some degenerate triangles I want to detect. It looks like a volume-less thin slits. If the question is not clear, please let me know.
4
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2answers
65 views

MInimizing cost function using iterative search for a minimum method

I want to estimated the parameters $\ \hat{\theta} $ of a model using an iterative search for the minimum of a cost function. The cost function is defined as follows: $$ V_N(\hat{\theta}) = \frac{1}{...
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0answers
39 views

Inverse Newton Method for optimization: is this the correct algorithm?

I am trying to implement the algorithm in this article. I have already asked a question before about it here, and I am trying to figure out what I am doing wrong. This time, it's this section of the ...
4
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1answer
121 views

Which optimization method can be used to do the following?

I've the following system of equations for studying information flow in the below graph, $$ \frac{d \phi}{dt} = -M^TDM\phi + \text{noise effects} \hspace{1cm} (1)$$ Here, M is the incidence ...
2
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2answers
97 views

What online optimisation algorithm can be used for a noisy cost function?

I am trying to optimise a function, but the function can be noisy and give varying results for the same parameters. Furthermore, it needs to be online, as the data from each new iteration happens ...
5
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1answer
110 views

Sensitivity of BFGS to the accuracy of the gradient

I am studying how to speed-up the BFGS method using quantum computing techniques. I have used a method of speeding up the gradient of the function, but it sacrifices the precision value of the ...
3
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2answers
196 views

log(det(X)) in Semidefinite Programming

I have been solving problems of the form $$max \ log(det(A)) \\ s.t. \ A = A^{T} \succeq 0, \\ p_{i}^{T}Ap_{i} \leq b_{i}$$ where $b_{i}$ and $p_{i}$ are input vectors (to be clear there is more than ...
4
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1answer
92 views

Arbitrary Precision Optimization Libraries?

Are there any well-known optimization libraries (ideally with Python bindings or even in Python) supporting (unconstrained) minimization (of $f:\mathbb{R}^n \to \mathbb{R}$ for $n$ for $n\sim 10^1,10^...
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5answers
603 views

Adjoint method for optimization problem

I am interested in the adjoint method for shape optimization problems. However, I couldn't find a helpful introduction. So I come here and look forward to some enlightening advices. Could you direct ...
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0answers
35 views

exploding gradients in gradient descent procedure of multi-output ridge regression

Multi-output ridge regression: $$W^{*}=\underset{W}{\arg \min } \frac{1}{\mathcal{N}}\|Y-WX\|_{F}^{2}+\lambda\|W\|_{F}^{2}$$ There are $Q$ outputs, $N$ samples, and $P$ covariates (features). $\hat{...
4
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1answer
72 views

How to form the following constraint in cvx?

The optimization problem is $$\min_{x\in K} \|h - x\|_2$$ where $$K = \{v\in R^n : \exists \lambda \geq 0\ v_1=v_2=\ldots=v_k=\lambda \ \text{and} \ |v_i| \leq \lambda \ \text{for} \ i=k+1,\ldots,n \...
3
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1answer
115 views

What are the advantages of Level Set method in topology optimization?

I am studying the different topology optimization methods. There are numerous resources out there but when it comes to comparing different algorithms, in terms of strengths and weaknesses most of ...
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0answers
89 views

Proving convexity of Frobenius norm and correlation function formulations of an optimization problem

I have been working on formulating my requirements in the form of an optimization problem in a multi-output regression setting. Firstly, I would like to make the variables I used in the problem and ...
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2answers
171 views

Is this a knapsack problem?

I have a set of $K$ keywords. Each of this keywords can have set of bids from $1\$,\dots,N\$$. For each bid for a keyword, it will get a specific amount of clicks and a specific cost. Clicks and Cost ...
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0answers
55 views

Are there unproblematic max constraints when modelling problems as Linear Programs?

Suppose we have a linear objective function that we want to maximize. All variables are from the set of reals. We have a constraint of the form: $$\max(x_1,x_2) + \max(x_3,x_4)\leq c\,, \text{ with } ...

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