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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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Best Possible Convex bounds for optimization problems

Suppose we have a primal problem $$ p^{*}=\min_x f(x), \\\text{s.t.}~~ h_i(x) \leq 0, $$ where $f(.)$ and $h_i(.)$ are possibly non-convex. Then its Lagrangian is $$\mathcal{L}(x,z_i)= f(x) + \...
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Nonlinear least square optimization

Problem description Given data at many time instance $t$, $$\min _{\alpha, \Lambda, \beta} \lVert y(t) - \alpha e^{\Lambda t} \beta \rVert_F$$ with $$ \lVert \alpha \rVert_2^F = 1 $$ where $y(t) \...
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Space covering optimization

I have the following problem: In the space $E=\{1, 2, \dots, N_x\} \times \{1, 2, \dots, N_y\}$, I want to define $N_R$ rectangles $R_k=\{x_k^0, \dots, x_k^1\}\times\{y_k^0, \dots, y_k^1\}$ which ...
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1answer
78 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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1answer
80 views

ill-conditioning

I am struggling with following exercise from book of Nocedal, Numerical optimization, chapter 2, excercise 2.12: Suppose that a function $f$ of two variables is poorly scaled at the solution $x^*$. ...
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2answers
136 views

How do I check if a loss function can achieve its minimum?

For example, the convex function $f(t)=e^{-t}$ doesn't achieve its minimum 0 on the real line. In a linear regression with $p$ predictors $X$, the loss function $f(\beta)=||Y-X\beta||^2$ achieves its ...
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1answer
86 views

Can I convert CUDA core to CPU core and use it as cpu core while running any program?

I was using Metatrader5 and have designed a strategy for trading using MQL5 programming language. While I was running a Strategy Optimization process, I saw the it will need 10,00= iterations or ...
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1answer
43 views

Conflicting definition of limit point

This question was raised at a different place without sufficient answers. Definition 1: We say that a vector $x \in R^n$ is a limit point of a sequence $\{x_k\}$ in $R^n$ if there exists a ...
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58 views

Methods for Parameter Scaling in Gradient-based Optimization

I am trying to minimize an objective function with 4 parameters, e.g., $a,b,c,d$ using gradient descent. $a < 0.1$, while $0 <b,c,d < 10$. I'm using a learning rate for all parameters on the ...
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205 views

What is required of the objective function in order to use Gauss Newton method?

From what I understand, the Gauss-Newton method is used to find a search direction, then the step size, etc., can be determined by some other method. In addition to that, are the following ...
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1answer
40 views

How to decide on what techniques to adopt in Genetic Algorithm optimization

Does it really matter which techniques we use in the process of GA optimization? For instance, if I use the Roulette Wheel Technique instead of Tournament Method for selection, two-point crossover ...
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1answer
280 views

I've developed a derivative-free optimization method, looking for comments

Here is the URL: https://github.com/avaneev/biteopt I've tested it on numerous global optimization benchmarking functions (included), and on real-world hyperparameter optimization problems I have. ...
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61 views

Clarifying a dual problem solution

I’m referring to this question on MathSE about the sum of $k$ largest eigenvalues of a symmetric matrix as an SDP-problem. I want to solve the maximization problem. As the dual problem is always ...
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1answer
116 views

How to numerically minimize a functional?

How to numerically minimize a functional, for example, $$J[y]=\int_{x_1}^{x_2}L(x,y(x),y'(x))dx$$ An equivalent problem is to solve the Euler equation for this functional as a differential equation. ...
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1answer
40 views

Distribute sources among destinations

There are $n$ sources with the following positive volumes: $p_1, ..., p_n$ and there are $m$ destinations with the following positive volumes: $q_1, ..., q_m$. It is known that $p_1+ ...+ p_n=q_1+ ...+...
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3answers
494 views

Finding the first N roots of transcendental equation

I need to find the first $n$ roots of the transcendental equation \begin{equation} F(k) = J_m'(kr)Y_m'(k)-J'_m(k)Y'_m(kr) \end{equation} for integer values of $m$ and any $r \in [0,1)$ where $J'$ ...
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1answer
239 views

Defining a soft constraint in cvxpy

I am using cvxpy to do a simple portfolio optimization. I implemented the following dummy code ...
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1answer
107 views

Nonlinear least-squares solvers vs. generic minimization

A nonlinear least-squares problem with $F:\mathbb{R}^m\to\mathbb{R}^n$, $$ F(x) \to \min_x \quad (\text{in the least-squares sense}) $$ really means minimizing $$ \frac{1}{2} \|F(x)\|^2 \to \min_x. $$ ...
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1answer
93 views

What is the fastest way to solve Ax=b (subject to constraints and an absolute term)

I am trying to solve/optimize $Ax=b$ in the least squares sense subject to box constraints; a few (less than 5) equality/inequality constraints; and an absolute function penalty (or some other ...
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0answers
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How does the MADS algorithm work in practice

Mesh Adaptive Direct Search (MASH) is an algorithm for black box optimization I want to understand an implement this method to solve some 2D multivariate blackbox function $f(x,y)$, but am having ...
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2answers
233 views

What is the most appropriate derivative free optimization algorithm

We can use random optimization/ derivative free/ direct search to find the minimum of some black box function $f$. If I have some 2D black box function, $f(x,y)$ - which I know to be convex - what ...
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1answer
177 views

Linear constraints for L-BFGS-B

I know L-BFGS-B only supports simple box constrains of the form: $l_i \leq x_i \leq u_i$, where $l_i$ and $u_i$ are constants. For my specific optimization problem, I need to specify some simple ...
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1answer
350 views

How to define the derivative for Scipy.Optimize.Minimize

I am trying to use scipy.optimize.minimize to minimise a quadratic objective function: $f(x) =x^\top Q x$. As a start, I have successfully implemented this using the built-in Nelder-Mead Simplex ...
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1answer
128 views

CPU and GPU influence on task parallel execution performance

This question is mainly about hardware, but also about software. In my current work I have approximately 68 millions of combinations that I am iterating through, in parallel. For each of those ...
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Genetic Algorithm: Need some clarification on selection and what to do when crossover doesn't happen

I'm writing a genetic algorithm to minimize a function. I have two questions, one in regards to selection and the other with regards to crossover and what to do when it doesn't happen. Here's an ...
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0answers
112 views

Levenberg-Marquardt for root-finding: just square the function?

This question might be so obvious and trivial that I'm having a hard time googling it. I have a multivariate root finding problem that I'm trying to solve in C# and the library that I'm trying to use ...
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1answer
54 views

reduced system: primal-dual interior point method for nonconvex constrained problem

When solving a reduced KKT system of a nonlinear (and nonconvex) constrained program after eliminating slack and dual variables, how do we actually take the next step in a primal-dual method? For ...
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0answers
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Procedure to identify characteristic properties of unknown functions in a DAE model

I have a system of 1st order odes given by $$ \dot{x_1}(t) = \alpha_1 f_1(x_1,t) + \beta_1 u(t) \\ \dot{x_2}(t) = \alpha_2 f_2(x_2,t) + \beta_2 u(t) $$ They are constrained by an algebraic equation ...
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0answers
58 views

overlapping additive schwarz [closed]

I am solving 1D laplace problem discretized with finite differences (3-point stencil). I would like to use additive Schwarz method in classical form: $U_{k+1}=U_{k}+M^{−1} r_k,$ where $r_k=F−A U_k$...
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1answer
80 views

Obtaining a feasible solution for underdetermined system of linear equations satisfying inequality constraints

I would like to obtain a feasible solution for an under-determined system of linear equations, $$Ax=b$$ where, $A \in \mathbb{R}^{7\times9}, \, x \in \mathbb{R}^{9\times1}\text{and } b\in\mathbb{R}^...
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159 views

Optimization of a blackbox function with an equality constraint?

I believe this would be an interesting problem. I have a blackbox function which can take 2-60 input variables $(X_1,X_2,...X_n)$ which are to be optimized. I'm calling this objective function as a ...
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1answer
60 views

Adaptive gradient descent

I want to minimize some multivariable function $\Delta(\alpha, \beta)$. I know that this function has a zero point, $\Delta(5, 5) = 0$. Starting from some $(\alpha, \beta)$ close to $(5,5)$ (e.g. (4....
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1answer
172 views

Ways to speed up solving an LP with Google's ortools

I'm having an issue solving an LP of the form: $$\min z = c^Tx$$ $$\text{s.t.}$$ $$Ax \geq b$$ $$x\geq p$$ $1 \leq a_{ij} \ll b_i$, $p \leq 0$, and $c \geq 0$ The specific problems I'm running into ...
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Are the No Free Lunch Theorems Useful for Anything?

I have been thinking about the No Free Lunch (NFL) theorems lately, and I have a question which probably every one who has ever thought of the NFL theorems has also had. I am asking this question here,...
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3answers
155 views

Optimization of a blackbox function

Let's say that we have an objective function $f(\mathbf x,\mathbf y)$ which has the parameters $\mathbf x=[x_1\ldots x_n]$ and $\mathbf y=[y_1\ldots y_n]$. Here, $\mathbf y$ is a blackbox variable ...
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Meaning of “points where the first- and second-order optimality conditions fail”

I am reading a paper that summarizes a set of simulations. Basically, the authors are trying to minimize some function using different optimization algorithms. They conclude: "Our findings point to ...
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1answer
71 views

Large-scale almost-linear optimization

I'm currently trying to minimize the following function: $$\min_{\mathbf x,\mathbf y,s} \|F\mathbf x-\mathbf a\|_2^2 + \|SF\mathbf y-\mathbf b\|_2^2 + \lambda \|\max(\mathbf x, \mathbf y)\|_1,$$ $$\...
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Sequential Quadratic Programming for Quadratically Constrained Quadratic Programs

A standard Quadratically Constrained Quadratic Program (QCQP) is of the form: $$ \underset{x}{minimize} \frac{1}{2}x^TP_{0}x + q_{0}^{T}x $$ $$ subject \; to \quad \frac{1}{2}x^TP_{i}x + q_{i}^{...
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1answer
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Free access to Xeon Phi clusters

Apologies if this is not the right forum to ask, but there has been a somewhat related question here. I am working on a piece of software (nonlinear constrained optimisation, coded in C++11 and ...
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2answers
66 views

Find combinations of variables with variable bounds to get summed up to a required value (using Python)

Let's say I have n variables. Each variable has lower and upper bounds. I want to find all suitable combinations of these variables to sum up to a required value. An example with two variables: $$ ...
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2answers
228 views

Knapsack problem with fixed number of elements?

I am looking at an optimization problem that looks like this: $$ \text{minimize}\;\; \mathbf{x}^TQ\mathbf x \;\;, \; \mathbf x \in \mathbb R^n, x_i \in \lbrace 0, 1 \rbrace\\ \text{subject to}\;\; ||...
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Obtaining the lagrangian multipliers in an optimization problem

Suppose we have this simple optimization problem \begin{align*} \underset{x\in V}{\text{min}} &~ f(x) \\ \text{s.t.}& ~x \leq \beta \end{align*} Using slack variables \begin{align*} ...
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63 views

Nonlinear global optimization algorithm that can use dynamic programming

I've asked this question on stackoverflow 2 weeks ago, but, judging by zero response, that probably was the wrong forum. Therefore copying it here: Let F0,...,Fn ...
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52 views

BFGS convergence problem

I would like first to state that it is beyond my capability to identify whether this is a BFGS issue or a R package problem. I've been doing some mixed logit regression using the R package "mlogit". ...
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1answer
249 views

Trajectory optimization for smoothness

I want to achieve the following in 2D (and without obstacles): Given start position A and end position B, generate the path between the two points that optimizes a cost function that depends on total ...
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1answer
115 views

pde-constrained optimization

I'm trying to solve a problem where I have a initial and final distribution of tumor, and my goal is to find the best map of parameters (diffusion and reaction terms) for a reaction-diffusion equation,...
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1answer
67 views

Solving for $C$ in $Q = YCZ$ using least squares in Matlab

I am trying to solve for the matrix $C$ in $Q = YCZ$ in matlab. I have preliminary results but they don't seem realistic. Here, $Q$ is $n \times m-1$, $Y$ is $n \times p$, $C$ is $p \times m$ and $Z$ ...
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45 views

Finding minimum of Wronskian determinant

If I have a partial differentail equation such as $\frac{\partial^2 u}{\partial t^2} + c^2 \frac{\partial^2 u}{\partial x^2}$, with boundary conditions $u(0,t) = 0$ and $u(1,t)=0$, I can solve this ...
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240 views

Using C/C++ for Markov chain Monte Carlo (MCMC) methods

I'm working on optimizing the parameters of a mathematical model to fit experimental data, using an existing formula for the likelihood of observing the data given a set of parameter values. At the ...
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3answers
149 views

How can I use Projected Gradient Descent for this optimization problem with constraint?

Suppose $ q $ and $ A $ are given and that $ q, p \in R^{N} $ and $ A \in R^{NxN} $, then how can I find the vector $ p $ such that $$ (q - p)^{T}A(q - p) $$ is minimized constraint to $ \sum_{i=1}^{...