Questions tagged [nonlinear-programming]
Questions about the theory and numerical algorithms for optimizing (minimizing or maximizing) nonlinear functions, possibly subject to equality and/or inequality constraints.
132
questions
0
votes
0answers
35 views
Nonlinear Optimisation and the Hartree-Fock Method [migrated]
I am revisiting some exercises in Thijssen's Computational Physics book, particularly chapter 4 on the Hartree-Fock method. I am interested in the method of nonlinear optimisation for its own purposes,...
0
votes
0answers
59 views
Phase portrait of non-linear system of ode (Triple Galaxy System)
Context: Hello, I am an undergrad student of physics self-studying Python Programming. I am trying to find the value of H_lam for which limit cycles corresponding ...
2
votes
0answers
34 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 ...
0
votes
0answers
18 views
How to multiply 2 decision variables and a matrix using python
So, basically our agenda is to assign tour guides to tour groups based on this equation and that will be done by these 2 decision variables z(u,g) and y(g,p) where z(u,g) will be 1 if tour guide 'u' ...
0
votes
0answers
29 views
Implementation of nonlinear optimization for Generalized Nash-Equilibrium
I have to find a solver for $\begin{equation}
\min_{x^{\nu}} \Theta_{\nu}(x^{\nu},x^{-\nu})
\end{equation}$ with $x^{\nu} \in X_{\nu}$ which is a convex set.
$x^{*}$ needs to satisfy $$\nabla_{x^{\nu}...
0
votes
0answers
24 views
Can Scipy.optimize take a user-defined objective function that contains an ML model?
I have an optimization task that requires me to choose the optimal combinations of parameters, according to the prediction of a random forest model. My main obstacle is that scipy.optimize always ...
0
votes
1answer
35 views
Avalability of SNOPT optimization solver
I'd like to know if SNOPT solver is available free of cost for academic research in any of the optimization software packages.
I came across a few softwares that have SNOPT, but those require a ...
4
votes
1answer
106 views
What's the right choice of variable settings for setting up my optimal control problem?
This is a followup to my previous question here
I have the following dynamical system,
$\frac{d \phi}{dt} = -M^TDM\phi \tag{1}\label{1}$
$\frac{d \hat\phi}{dt} = -M^T\tilde{D}M\hat \phi \tag{2} \...
3
votes
1answer
158 views
Setting up optimization problem in GEKKO
I have the following dynamical system,
$\frac{d \phi}{dt} = -M^TDM\phi \tag{1}\label{1}$
$\frac{d \hat\phi}{dt} = -M^T\tilde{D}M\hat \phi \tag{2} \label{2}$
$\eqref{1}$ represents the exact ...
3
votes
0answers
130 views
Automatically generate constraints for trajectory optimization
This is a follow up to my previous post here
I'm interested in performing trajectory optimization from the problem mentioned in abov link.
I want to supply the following as dynamical constraints to ...
1
vote
2answers
109 views
Solving a parameter estimation problem using trajectory optimization
This is a follow-up to my previous question here
I've the following system of equations for studying information flow in the below graph,
$$ \frac{d \phi}{dt} = -M^TDM\phi + \text{noise ...
3
votes
0answers
73 views
Large-scale optimization of nonlinear equations
I'm looking to find a computationally efficient solution to a large system of nonlinear equations. I'm trying to maximize the following function:
$$ f(\vec{x}) = \sum_i^N C_i (x_i-A_i)x_i^{\epsilon_{...
5
votes
2answers
229 views
MINLP with GEKKO - Modeling discrete variables
I'm trying to define a MINLP optimization problem with GEKKO in Python, and I want to use some variables with fixed values.
For my first variable, x1, I need to define the following values (as would ...
1
vote
1answer
202 views
Preconditionning for solving a non-linear system of equations with least squares
I am trying to solve a large system of non-linear equations (about a few hundred equations and variable but with less variable than equations). Given that the system is really sparse and large I am ...
3
votes
0answers
28 views
How to set up and solve acceleration-limited trajectory optimization problems?
I've been trying to learn how to solve simple acceleration-limited trajectory planning problems. I'm working in C++ and I've been using the Eigen library to do linear systems solving. I'm doing the ...
3
votes
1answer
75 views
Geometric Programming - symbolic version
I am interested in finding minimizers of functionals of the type $\sum x^ay^bz^c$ where the exponents are 1, 0 or -1. I have codes to find such minimizers when they exist up to machine precision, ...
1
vote
0answers
50 views
Finite dimensional optimization problem over dynamical system
I am interested in solving numerically the following mathematical problem
Consider an ode of the form
$$
\dot q(t) = f(q(t),t_1,\ldots, t_N),\qquad t\in [0,T],
$$
where $q\in \mathbb{R}^n$ is the ...
2
votes
1answer
83 views
Nonlinear conjugate gradient with orthogonality constraint
I have to solve a set of nonlinear optimization problems in the subspace defined as the orthogonal space to a given vector.
More precisely,
$$
\arg\min f(\vec x) \qquad \text{with} \qquad \vec x \...
1
vote
0answers
97 views
Connection between piecewise linear basis functions and RELU activation function
ReLU activation is defined as follows
$$\sigma(x)=\max(0, x).$$
Let's assume that I have deep network of 1 hidden layer, than output from my layer has form
$$ f(x)= \sigma(Wx +b), $$
where matrix W ...
1
vote
1answer
58 views
Research articles on MultiObjective Non-Linear Programming (MONLP)
I'm looking for papers dealing with multi-objective non-linear programming which could help me implement an algorithm to solve my problem.
My problem is :
Maximize $f(x) = c \cdot x$, while ...
-3
votes
1answer
109 views
0
votes
1answer
42 views
Slightly change two vectors to satisfy a constraint
$\vec{a}\cdot\vec{b} \approx c$
$\vec{\alpha} \cdot \vec{\beta} = c$
$\vec{\alpha}$ is close to $\vec{a}$ and $\vec{\beta}$ is close to $\vec{b}$
Given $\vec{a}$, $\vec{b}$ and c, how to find $\vec{\...
1
vote
1answer
371 views
Why does Newton's method with Linear Equality Constraints use KKT condition?
Goal: Optimize convex function $f(\vec{x})$ subjected to constraint $A\vec{x} = \vec{b}$ starting at a point $\vec{x}_0$ that satisfies the constraint.
The problem only has equality constraint. Why ...
1
vote
1answer
315 views
Piecewise-Linear Quadratic Optimization for an “Almost Convex” Problem
I have a 7-14 dimensional piecewise linear cost function I'd like to minimize with two quadratic terms of the form:
$$
f(X) = X^tCX + d \sum_i |x_i-x^*_i|^2 + \sum_i P_i(x_i-x^0_i)
$$
$$ \sum_i x_i ...
2
votes
0answers
279 views
Convergence of a very large non-linear least squares optimization
(note: I also posted this question on stackoverflow before finding this community here, which seems a better place for it)
I'm trying to solve the following problem: I have a lot (~80000) surface ...
4
votes
1answer
252 views
How to solve the following Frobenius norm-minimization problem?
Background
We know how to solve the following minimization problem
$$
\min_{X} \lVert AX - B \rVert_F^2
$$
But what about the extended version?
$$
\min_{X} \lVert A
\begin{bmatrix}
X & X^2
\...
1
vote
0answers
71 views
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) \...
2
votes
1answer
31 views
Constraints 'exactly/at most one non-zero element' without binary variables
In a much larger MINLP problem, I have set of variables $\{a_{ij}\}_{m,n}$, such that $0 \leq a_{ij} \leq 1 $ for all $i$, $j$, which I could think of as a matrix, for which I have two requirements:
...
0
votes
1answer
59 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 ...
3
votes
1answer
147 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}^...
2
votes
0answers
53 views
Nonlinear Sylvester-Like Equation
Maybe you can point me to some results already developed for this.
I have to solve for $X$ the following "Sylvester-like" equation:
$$ AX - XB = F(X)$$
where $A\in\mathbb{R}^{a\times n}$, $B\in\...
1
vote
0answers
78 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 ...
0
votes
1answer
66 views
Why can't we discretize continuous domains in distributed non-convex constraint optimization problems?
Consider a non-convex distributed optimization problem.
We have $X$ = a set of $n$ decision variables: $x_i$ where $i=1..n$ and $x_i \in R$, the set of Reals.
We have $F$ = a set of $m$ constraint ...
1
vote
1answer
131 views
Correct way to model an embedded reinforcement (non linear FEM)?
I need to add to an existing FEM solver some embedded reinforcement element. This would give me the possibility to model/solve concrete structure (reinforced with steel rebar) taking into account the ...
3
votes
1answer
296 views
Why do active set methods or the simplex method pivot only one variable at a time?
Why do active set methods or the simplex method pivot only one variable at a time? Ostensibly, we could add multiple columns to the basis during pivoting, but the standard presentation of the methods ...
1
vote
2answers
92 views
How can i solve this non-convex multi-variable optimization problem?
I want to solve the following optimization problem:
$$\min_{A,B,X} \|Y-AX\|_F^2 + \lambda_1 \|Z-BX\|_F^2+ \lambda_2 \|B\|_F^2$$
$$s.t ~~x_{ij}~ \geq 0$$
in which, $Y$ and $Z$ are data matrices and ...
5
votes
1answer
93 views
$L_2$ projection with integer constraints and prescribed sum
Suppose I am given a vector $v^0\in\mathbb{R}^n$ and integers $k,\ell\in\mathbb{Z}$. Assuming $k$ is close to zero (e.g. $0\leq k\leq5$), is there an algorithm for solving the following integer ...
1
vote
0answers
56 views
Object-oriented non-linear solving in python
I'd like to build a system in Python, consisting of (broadly speaking) objects which are internally described with (not necessarily just linear) equations, that I can connect with each other - similar ...
1
vote
0answers
71 views
Minimizing the products of variables
My problem
Maximize
$$\min_{i} \{\ c_i \cdot \prod_{j \in A(i)} {x_{j}} \prod_{j \in B(i)} {y_{j}} \} $$
Subject to
\begin{align}
&\sum_{j \in C(k)} x_{j} = 1,\ \forall k \\
&l \leq x_{j}...
2
votes
1answer
423 views
Optimization of multiple functions
I have 3 functions which consist of 6 variables $p_1,p_2,p_3,p_4,p_5,p_6$. The value of each function is equal to $x$ (say):
\begin{align}
f_1 &= \operatorname{sign}(2-p_1) \sqrt{|2-p_1|} + \...
1
vote
0answers
229 views
Constrained optimization: Stationary point vs. Nash point
1s question: definition of stationary point for constrained optimization
As far as I know, a stationary point of a constrained optimization problem is a stationary point of the Lagrangian (that has ...
1
vote
0answers
48 views
How is the Gastner-Newman equation implemented to create value-by-area cartograms?
There is a paper called "Density-equalizing map projections: Diffusion-based algorithm and applications" by Michael T. Gastner and M. E. J. Newman, which explains their algorithm (which is based in ...
0
votes
1answer
331 views
Example Problem to Demonstrate BiCGStab
So our team has been able to code up a BiCGStab implementation for a class project, and we'd like a potential example problem to try it out on.
So far, we've talked about a 1D Laplacian with Neumann ...
5
votes
1answer
143 views
Largest hypercuboid inside a polyhedron
Given a polyhedron $\mathbf{Ax} \leq \mathbf{b}$, how to find the largest hypercuboid, with unknown center $\mathbf{x_{0}}$ and side lengths $2\epsilon_{i}$, which are aligned along the co-ordinate ...
0
votes
1answer
6k views
Backtracking-Armijo Line Search Algorithm
EDIT based on comments below:
I add the mathematical formulation of my problem below. I am trying to solve an equation of the form
$$
\partial_t f(x,y,t)= (\partial^2_x +\partial^2_y) f(x,y,t) \equiv ...
1
vote
2answers
43 views
Optimisation of purely integer quantity with bound-constraints for a 1D expensive function whose analytical form is not available
I have a computationally expensive objective function, whose analytical form is not available. The only input argument to the objective function is an integer variable. The goal is to compute the ...
3
votes
2answers
66 views
Gradient of function after renormalization of variables
I have to minimize a function $f(\mathbf{x})$, where the vector $\mathbf{x}\in\mathbb{R}^n$ satisfies $|\mathbf{x}|=1$. So I tweaked the code of $f$ so that it renormalizes $\mathbf{x}$ as the first ...
0
votes
1answer
98 views
Perturbation in bounds given the perturbation to constraints
Given a feasibility problem with both inequality and equality constraints, I'm interested in the sensitivity of the bounds of the region to changes in the constraints. To help with answering the ...
2
votes
0answers
66 views
Global optimization with known distributions of some variables
I'm solving simple single-objective multidimensional global optimization problem using various stochastic algorithms like Monte-Carlo, GA and other evolutionary approaches. The task is formulated as ...
1
vote
0answers
97 views
2D Multilateration with constraints as distance information
In my current problem, I am looking for an algorithm to reconstruct the position of multiple points in the 2D euclidean plane with incomplete distance information, $d_{ij}=||x_j-x_i||\in\text{dom}_a$.
...
