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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2
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45 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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One to Many Assignment Problem where agents can't be assigned the same task if they share a trait

In the one to many assignment problem, many agents can be assigned to a task. In my problem, I want to assign many agents to a task, but for all of the agents assigned to that task, they must share a ...
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48 views

What is going wrong in the way I set up constraints for optimization solver?

I have the following dynamical system, Equation 1: $\frac{d \phi}{dt} = -M^TDM\phi$ Equation 2: $\frac{d \hat\phi}{dt} = -M^T\tilde{D}M\hat \phi$ Equation 1 represents the exact dynamics of a ...
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1answer
38 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.
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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 ...
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2answers
54 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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27 views

Pseudocode for implementing Tunneling Algorithm

I‘m searching for coding examples (Python, matlab, C++, ...) of the tunneling algorithm for finding global minimums in terms of Optimization. Even though some Users have claimed to implement the ...
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1answer
117 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
85 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 ...
3
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2answers
166 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
71 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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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{...
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1answer
62 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 \...
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1answer
86 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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45 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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15 views

SHREC 2010 Descriptors

I will appreciate if I may find someone how can clarify for me the part regarding the quality of feature descriptor, shown in the figure below: and this screenshot is from the article: SHREC All my ...
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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 ...
3
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1answer
57 views

Evaluate 3D Shape Descriptor

I'm trying to create my own 3d shape descriptor, the idea is that how I may evaluate how much my descriptor is well and good? What I checked is that they evaluate descriptors through shape matching, ...
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0answers
59 views

Conjugacy in Non-linear Conjugate Gradient Descent

In linear conjugate gradient method, our goal is to solve the system of linear equations $$Ax = b$$ where A is a symmetric positive definite matrix, and that is equivalent to finding the minimizer of ...
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How to use the solution of a multistage stochastic program?

Given a multistage stochastic program, its solution (if it exists) consists of the first decision vector, as well as all the recourse decision vectors for all possible scenarios of an event tree. But ...
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1answer
200 views

When training a neural network, why choose Adam over L-BGFS for the optimizer?

More specifically, when training a neural network, what reasons are there for choosing an optimizer from the family consisting of stochastic gradient descent (SGD) and its extensions (RMSProp, Adam, ...
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1answer
50 views

Could the convex problem be tackled by CVX?

I want to solve the convex optimization as follows: \begin{align} \underset{X_1,X_2}{\min} &\ -\frac{1}{N}\sum_{i=1}^N\log\det\left(I+H_i^HX_2H_i\right)-\log\left[1+h^H(X_1+X_2)h\right]\\ &\...
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40 views

Fitting a plane with the Prewitt gradient operator

Prewitt gradient operator Show that the Prewitt gradient operator can be obtained by fitting the least-squares plane through the 3 × 3 neighborhood of the intensity function. Hint: Fit a plane to ...
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41 views

Nonlinear least squares optimized Jacobian calculation

I have a nonlinear least squares problem, in which I am trying to minimize residuals which can be divided into four classes: $$ \min_x ||\epsilon(x)||^2 + ||\xi(x)||^2 + ||\delta(x)||^2 + ||s(x)||^2 $$...
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1answer
69 views

Prove that the set of maximizers are independent of parameter in the objective function

A maximization problem reads as $$ J(y) = \sum_{k=1}^{K} \sigma_k(y)^q \mathop{\rightarrow}^{y} max$$ where $q \in [1,\infty]$ is a user-defined parameter and functions $\sigma_k, k=\{1,\dots,K\}$ ...
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51 views

Methods to approximate obective function gradients from point cloud

Problem statement: Assume that I have an objective function $f(x)$ which takes as input a $D$-dimensional vector $x\in\mathbb{R}^D$, and that $f(x)$ is sufficiently smooth. Assume further that I ...
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66 views

How to approach geographic data interpolation by distance?

let's say I have a set of geographic locations (lat, lng) resulting from a query. Those locations have some kind of internal ranking, my set is sorted by this number in a descending order. Now I'm ...
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1answer
56 views

How to obtain only the value of my variable using scipy.optimize.minimize

when I minimize a function using scipy.optimize.minimize I get a big list of things as a result, but I would like to only get the value of my variable, this is my code : ...
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55 views

Multilevel minimization - boundary conditions

I am interested in minimizing $$min_{x \in R^{n^l}} f^l(x),$$ where $f^l(x)$ is nonlinear objective function arising from discretization of PDE. I would like to use nonlinear multilevel minimization ...
3
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1answer
68 views

Calculate Transformation Matrix between two sensors

My question is if I can calculate the transformation matrix between two sensors. Each sensor provides a $4\times 4$ matrix for every timestep recorded. The sensors are moving and have some noise in ...
3
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2answers
54 views

Optimal line such that maximum points are between an upper and lower boundary

I have some 2D data and would like to find a line $y = mx + b$ such that a maximum number of points from the data is captured within the area between $y = mx + b + margin$ and $y = mx + b - margin$. ...
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2answers
138 views

How to solve calculus of variations problems numerically?

For example, how to solve the well-known isoperimetric problem (i.e., to enclose the largest area with a fixed-length curve)? We can simplify things a bit and fix the two ends of the curve at $[a,0]$,...
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30 views

Method for implementing QP solver with matrix terms?

I am trying to implement (in code) a QP solver for the following equation: $$\min_{u} u^{T} Wu$$ $$s.t. \; \beta u = \tau_{ref}$$ $$ Au \leq b $$ See this document, section 5.1 (Page 35) $u$ is a ...
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66 views

Ramp least squares estimation

With some given $s$ value, let \begin{equation} \begin{aligned} h(\beta)&=\min(\sum_{i=1}^n(Y_i - X_i\beta)^2, s)\\ &=\sum_{i=1}^n(Y_i - X_i\beta)^2-\max(0, \sum_{i=1}^n(Y_i - X_i\beta)...
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1answer
56 views

Python-accessible industry-standard for unconstrained minimization that converges to machine precision?

I have an unconstrained minimization problem of many variables for which I know the gradient exactly. I turned to the conjugate gradient method contained in ...
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0answers
46 views

Interesting maxmin mixed integer/real quadratic optimization problem

I have the following problem: $ \DeclareMathOperator*{\argmax}{arg\,max} \DeclareMathOperator*{\argmin}{arg\,min} \argmax_{\underset{\lambda_k\in \mathbb{R}}{\sigma_q^2(k)\in \mathbb{R}}} \left[\...
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1answer
54 views

ADMM: why does method of multipliers lose decomposability

I am trying to understand intuition of ADMM (alternating direction methods of multipliers). It combines dual ascent and method of multipliers. Downside of method of multiplier is the loss of ...
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90 views

Minimum of quadratic assignment (QAP) with convex objective

Suppose $A,B\succeq0$ and $C\in\mathbb R^{n\times n}$. I am hoping to solve an instance of the following optimization problem: $$ \min_{\textrm{permutation matrices }P} \mathrm{tr}(BP^\top AP+C^\top ...
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46 views

Least square approximation of a polynomial with a constraint on the derivative in Python

I'm trying to fit a polynomial of the third degree through a number of points. This could be a very simple problem when not constraining the derivative. I found some promising solutions using CVXPY to ...
3
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1answer
89 views

Can I solve a model in GEKKO with Black Box, Simulated Annealing or GA solvers?

I'm trying to use my current GEKKO model with different solvers methodologies. I don't know if I can also use global optimisation solvers as GA, Simulated Annealing o Differential Evolution. I need ...
4
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1answer
40 views

Binary combinatorial optimization with hard to compute costs

I have a combinatorial problem regarding the optimal placement of sensors. I want to find the optimal placement of $N$ sensors, given $M$ possible locations, $N < M$. Right now I'm working with ...
6
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1answer
95 views

Optimization algorithm / approach for suggesting what goods to buy and sell in a marketplace?

A toy problem would probably be best to explain it this. Let's say we have 100 people, each with 4 unique types of items (to simplify things, let's say it's the same four types of items for each ...
3
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1answer
373 views

What's the terminology for this alternative minimization algorithm?

Say the model is $F(x_1)G(x_2)Z(x_3) = y \in \mathbb{R}^N$, with $F,G,Z$ explicitly known, we are given observation of $y$ as $y_b \in \mathbb{R}^N$ to find the value of $x_1$, $x_2$, $x_3$ for each ...
5
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1answer
99 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
161 views

How to include penalty in a Objective Function with Python? GEKKO

I'm trying to include a "great M" penalty in my objective function. I want use the entry x vector values as entry values in a function. A fixed maximum value is took initially for the returned value ...
4
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2answers
142 views

MINLP with GEKKO - Modeling discrete variables

I'm trying to define a MINLP optimization problem with GEKKO in Python, and I want to use some variables with fixed values. For my first variable, x1, I need to define the following values (as would ...
3
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1answer
57 views

Optimizing for multiple objectives

Optimizing two models here, each model having its own set of parameters and an objective, but both models run on the same data which is difficult to compute, and which is computed based on both models'...
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0answers
43 views

Classification of multiobjective optimization algorithms

I am looking for a good (canonical?) overview paper(s)/book(s) on the classification of multiobjective optimization algorithms. I am focused on obtaining a representative set of Pareto optimal ...
4
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1answer
124 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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106 views

A maximization problem, with motivation in machine learning

Consider the minimization problem described this paper. Let $f_{\lambda}$ be the minimizer. As a part of extending my work, I am able to show the following facts $$\lim_\limits{\lambda \to 0}\|f_{\...

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