Questions tagged [constrained-optimization]
Questions about optimization problems subject to additional constraints.
0
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0answers
2 views
Can LINCS algorithm be used for colliding molecules?
Supposing that one molecule is static and one is dynamic,
can the dynamic one be solved with LINCS for its shape (angle, bond length) constraints and also keep collisions with static molecule off, ...
0
votes
1answer
36 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 ...
3
votes
1answer
32 views
Improve optimization speed for a set of similar problems: Quadratic programming with a warm start
I am repeatedly solving quadratic program,
$x^T Q x$ with time dependent linear constraints $Ax=b_t$.
Dimension of $x$ is around 10000 and there are around 50 constraints. I want to solve the ...
1
vote
1answer
42 views
sum of absolute difference constraint in optimization problem
I am writing a model for an optimization problem. I need to write the following constraint:
$$\sum^{N - 1}_i \lvert (a_i - a_{i+1}) \rvert \leq 2\, .$$
How to write this constraint (or linearize)?
...
1
vote
1answer
40 views
Nature of stationary points of a Lagrangian fuction
I would like to extremize a certain function $f$ with respect to a parameter $x$, under constraints $g_1(x) = 0, ..., g_m(x)=0$. In order to achieve this, I consider the Lagrangian function $L(x, \...
1
vote
1answer
33 views
Techniques to remove a function from Levenberg-Marquardt when it is against box constraints
I have a somewhat large (20+ dimensional) root finding problem that I'm solving with Levenberg-Marquardt. One of the functions has box constraints on [0, 2]. When it is against those bounds it will ...
1
vote
1answer
67 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
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0answers
29 views
Logging vs outputs in iterative optimisation
I'm coding an iterative algorithm of constrained continuous optimisation. An augmented Lagrangian algorithm (outer) calls a bound-constrained L-BFGS-B algorithm (inner), which calls a line search ...
2
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0answers
30 views
Sensitivity Lagrangian solution general case
I have asked this question already on maths and mathoverflow.
Just a question about a literature reference. I am writing a paper for engineers.
Usually for the Lagrange multiplier problem
$$
\...
1
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0answers
30 views
Efficient numerical optimization of an “almost separable” function
I have come across an optimization problem with the following objective function:
$$f(x_0,y_0,z_0,x_1,y_1,z_1,...,x_N,y_N,z_N) = \sum_{i=0}^N f_i(x_i,y_i,z_i, \alpha(x_{i+1}-x_i) + \beta(y_{i+1}-y_i))...
6
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0answers
109 views
An invertible matrix that minimizes the norm of the product with a given matrix
Given a fat matrix $B \in \mathbb{C}^{n \times m}$ (where $m > n$) with full row rank, I would like to find (numerically) a full-rank matrix $A$ that minimizes the Frobenius norm of the product $A ...
3
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1answer
188 views
How does fmincon in MATLAB calculate gradients?
I am trying to solve numerically a constrained optimisation problem in MATLAB, and I am wondering how the fmincon function calculates gradients when one isn't ...
1
vote
1answer
68 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:
$$
f(X) = X^tCX + d \sum_i |x_i-x^*_i|^2 + \sum_i P_i(x_i-x^0_i)
$$
$$ \sum_i x_i ...
-1
votes
1answer
70 views
Simulation-based Optimization vs PDE-constrained Optimization
What is the difference between Simulation-based Optimization and PDE-constrained Optimization?
Would studying a text on Simulation-based optimization be sufficient to understand and apply both?
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0answers
41 views
Metric selection and scale-based preconditioning in quadratic optimization problem
I'm going to use scale-based preconditioning in a quadratic optimization problem: minimize $ x^T Q x + p^T x$ such that $ A x + b = 0$ and $D x + E \leq 0$, I want to speed up finding the optimal $x$ (...
2
votes
0answers
134 views
Convergence of a very large non-linear least squares optimization
(note: I also posted this question on stackoverflow before finding this community here, which seems a better place for it)
I'm trying to solve the following problem: I have a lot (~80000) surface ...
5
votes
1answer
88 views
Solvers for Quadratically Constrained Quadratic Programs (QCQP) with complex variables
I'd like to know whether there are any publicly available tools for solving QCQP with complex variables (and constraints therefore expressed through Hermitian matrices). What I have found so far is ...
1
vote
1answer
181 views
How to use CSDP to express a semidefinite program?
I am trying to use CSDP and am struggling with it. Consider, for example, the following semidefinite program
$$\begin{array}{ll} \text{minimize} & 0\\ \text{subject to} & Q - A' Q A - \...
1
vote
1answer
162 views
FMINCON Step Size Tolerance
I get following error after implementing the attached code.
Error Message
"fmincon stopped because the size of the current step is less than
the default value of the step size tolerance but ...
1
vote
0answers
39 views
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 ...
3
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2answers
135 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 ...
2
votes
1answer
25 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:
...
6
votes
1answer
203 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 ...
1
vote
1answer
294 views
How to Solve Optimisation Problems using Penalty Functions in Python
I am working on a implementing a simple quadratic optimisation problem:
$$\min _x \; {\underline{x}}^T Q {\underline{x}}$$
$$s.t. \,\quad {\underline{\mu}}^T{\underline{x}} = R^*$$
$$ \quad \quad \...
0
votes
1answer
204 views
Defining a soft constraint in cvxpy
I am using cvxpy to do a simple portfolio optimization.
I implemented the following dummy code
...
2
votes
1answer
92 views
What is the fastest way to solve Ax=b (subject to constraints and an absolute term)
I am trying to solve/optimize $Ax=b$ in the least squares sense subject to
box constraints;
a few (less than 5) equality/inequality constraints; and
an absolute function penalty (or some other ...
1
vote
2answers
64 views
Making difference of log constraints convex
I have the discrete likelihood estimation problem $\max \sum m_i\log p_i $ where $m$ is a given vector of length $n$. The constraints are $0 \preceq p \preceq 1$, $\sum_{i=1}^n p_i = 1, $ and one ...
3
votes
1answer
157 views
Linear constraints for L-BFGS-B
I know L-BFGS-B only supports simple box constrains of the form: $l_i \leq x_i \leq u_i$, where $l_i$ and $u_i$ are constants. For my specific optimization problem, I need to specify some simple ...
0
votes
1answer
54 views
reduced system: primal-dual interior point method for nonconvex constrained problem
When solving a reduced KKT system of a nonlinear (and nonconvex) constrained program after eliminating slack and dual variables, how do we actually take the next step in a primal-dual method?
For ...
4
votes
1answer
79 views
Obtaining a feasible solution for underdetermined system of linear equations satisfying inequality constraints
I would like to obtain a feasible solution for an under-determined system of linear equations,
$$Ax=b$$
where, $A \in \mathbb{R}^{7\times9}, \, x \in \mathbb{R}^{9\times1}\text{and } b\in\mathbb{R}^...
1
vote
0answers
137 views
Optimization of a blackbox function with an equality constraint?
I believe this would be an interesting problem.
I have a blackbox function which can take 2-60 input variables $(X_1,X_2,...X_n)$ which are to be optimized. I'm calling this objective function as a ...
4
votes
1answer
147 views
Ways to speed up solving an LP with Google's ortools
I'm having an issue solving an LP of the form:
$$\min z = c^Tx$$
$$\text{s.t.}$$
$$Ax \geq b$$
$$x\geq p$$
$1 \leq a_{ij} \ll b_i$, $p \leq 0$, and $c \geq 0$
The specific problems I'm running into ...
1
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3answers
155 views
Optimization of a blackbox function
Let's say that we have an objective function $f(\mathbf x,\mathbf y)$ which has the parameters $\mathbf x=[x_1\ldots x_n]$ and $\mathbf y=[y_1\ldots y_n]$. Here, $\mathbf y$ is a blackbox variable ...
1
vote
0answers
52 views
Sequential Quadratic Programming for Quadratically Constrained Quadratic Programs
A standard Quadratically Constrained Quadratic Program (QCQP) is of the form:
$$ \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}^{...
2
votes
2answers
214 views
Knapsack problem with fixed number of elements?
I am looking at an optimization problem that looks like this:
$$
\text{minimize}\;\; \mathbf{x}^TQ\mathbf x \;\;, \; \mathbf x \in \mathbb R^n, x_i \in \lbrace 0, 1 \rbrace\\
\text{subject to}\;\; ||...
4
votes
0answers
35 views
Obtainting KKT for QSDP for the trace inequality constraint
I am working on developing my own solver(for implementation on hardware), based on IPM for following problem:
\begin{equation}
\begin{split}
\min_{X} \; \frac{1}{2}&\|X\|_F^2 + trace(CX)\\
\text{...
1
vote
0answers
88 views
functional second derivative
I'm trying to build a numerical solution for a parameter estimation problem of reaction-diffusion equation, using the adjoint method.
To summarize it, I'm trying to minimize the function
$$ G=\frac{...
4
votes
0answers
105 views
How to apply an integrated constrain condition in FEM?
I'm running some simulation using FEM. In my model I need to apply a constraint condition to the governing equation. My governing equation similar to the diffusion equation as below:
$$\frac{\...
1
vote
0answers
47 views
Obtaining the lagrangian multipliers in an optimization problem
Suppose we have this simple optimization problem
\begin{align*}
\underset{x\in V}{\text{min}} &~ f(x) \\
\text{s.t.}& ~x \leq \beta
\end{align*}
Using slack variables
\begin{align*}
...
5
votes
1answer
91 views
Constrained simulated annealing
Simulated annealing is a useful technique for finding near-optimal solutions to combinatorial problems. I have found a lot of tutorials on implementing the basic algorithm, but miss a general guide as ...
4
votes
1answer
223 views
adjoint method for reaction-diffusion problem
I'm trying to code a parameter estimation for a reaction-diffusion problem. Namely, knowing the distribution of tumor density $u$ at time $0$ and $T_f$ ($u^0$ and $u^f$), what are the best ...
0
votes
1answer
115 views
pde-constrained optimization
I'm trying to solve a problem where I have a initial and final distribution of tumor, and my goal is to find the best map of parameters (diffusion and reaction terms) for a reaction-diffusion equation,...
1
vote
0answers
62 views
What are the numerical properties to consider between Augmented Lagrangian and the Penalty Method?
I'm interested in (locally) minimizing a smooth nonconvex objective function:
$$
f(\textbf{x}_1, \textbf{y}_1,\cdots, \textbf{x}_n, \textbf{y}_n)=\sum_{i=1}^ng(\textbf{x}_i, \textbf{y}_i)
$$
Subject ...
1
vote
1answer
234 views
Minimization of least square function together with a nonlinear function
I have the linear underdetermined system $$Ax=b$$ and I need to find $x$ constrained by the maximization of a score function $g(x)$.
I could find the minimum of a function like $$|Ax-b|^2+1/g(x)$$ ...
4
votes
3answers
147 views
How can I use Projected Gradient Descent for this optimization problem with constraint?
Suppose $ q $ and $ A $ are given and that $ q, p \in R^{N} $ and $ A \in R^{NxN} $, then how can I find the vector $ p $ such that
$$ (q - p)^{T}A(q - p) $$ is minimized constraint to $ \sum_{i=1}^{...
3
votes
1answer
149 views
Why do active set methods or the simplex method pivot only one variable at a time?
Why do active set methods or the simplex method pivot only one variable at a time? Ostensibly, we could add multiple columns to the basis during pivoting, but the standard presentation of the methods ...
3
votes
1answer
308 views
Scaling/nondimensionalization for numerical optimization
I have a numerical optimization problem that I am trying to scale appropriately, in order to allow for the solver to achieve faster and more accurate results. I found a paper here that had a short ...
3
votes
1answer
71 views
Description of algorithm for small scale linear least squares with box constraints
I have small scale dense least squares problem with box constraints
$$\mbox{argmin}||Ax - b||^2 \quad $$
$$\mbox{subject to} \quad l_i \leq x_i \leq u_i,$$
Number of variables is about 10-50, ...
1
vote
2answers
84 views
How can i solve this non-convex multi-variable optimization problem?
I want to solve the following optimization problem:
$$\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 ...
2
votes
0answers
81 views
Minimize number of rectangles that cover all the points
I have a 2d distribution of moving points with known trajectories represented in a 640x480 image. Here is the initial state:
I have to find the minimum number of rectangles with fixed dimensions (...
