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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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Possible application of Polya's urn on real data (for Portfolio optimization)

I wanted to find some more information of this topic, but I found very little. I might be interested in optimizing a stock investment portfolio. Maybe I could use beta or some other common risk ...
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21 views

Examples of problems that cannot be formulated as optimization problems

An optimization problem has 3 main components: decision variables, constraints and an objective function. Such a problem can be mathematically modelled and solved using an optimization solver. For ...
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1answer
50 views

Solve multi-dimensional optimization problem using basinhopping

I am searching for an optimization solution, which is a 8d vector representing 4 complex elements, where each element is within the complex circle with maximal radius 1.2. The objective function is: ...
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2answers
73 views

Optimization techniques for expensive multi-variable functions

I'm working with a finite element model in which I'm interested to minimize the average temperature at a surface. I have 15 independent variables in my model, including geometry, materials, flows, ...
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3answers
115 views

Find a solution of large system of inequalities

I have a large system of homogenous inequalities involving 33 real unknowns of the form $$ \vec{F}(z_i)^T \cdot \vec{X}>0\, $$ where $\vec{X} = \left(x_1,...,x_{24}\right)^T$ are the unknowns and ...
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42 views

Non-parametric models as solutions to Partial Differential Equations

In the realm of scientific computing, there are a plethora of techniques developed to solve Partial Differential Equations (PDEs). Many of the popular methods are variants of common techniques such as ...
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1answer
59 views

What is the name for this type of constraint?

I have what would be a straightforward mixed-integer linear programming problem, except for the fact that some of the constraints are of the form $f(x_1,x_2,x_3,\ldots,x_n) < c$, where $f$ is 'take ...
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12 views

Cost functions to judge time/memory/accuracy tradeoffs

I am working on an interesting algorithm: Its absolute error is exponential in a parameter $j \in \mathbb{N}$, and for a given $j$, I have complete freedom to choose between an $\mathcal{O}(1)$ time-...
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90 views

How to solve extremely large scale linear system

I have a extremely large scale liner system equation. It stems from the minimization of $$ E(\{\widetilde{C_{i}}\}) = \sum_{i}\left[ \left( 1-\psi _{i}\right) \left\| \widetilde{C_{i}}-\sum_{j\in{N_{i}...
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1answer
16 views

Minimize squared error of linear function

Let $M$ be a $m \times n$ matrix, $x$ a $n$-vector, $y$ a $m$-vector, and $\|\cdot\|_2$ represent the $L_2$ norm (i.e., Euclidean norm). Given $M,y$, the goal is to find $x$ that minimizes the ...
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27 views

Minimizing the ratio of two specific non negative quadratic convex functions

$F$ is $m\times m$ diagonal, with real non negative elements $D$ is $n \times m$ complex $P$ is $n \times 1$ complex $A$ is $m \times 1$ complex. Minimize $\Gamma(A)$, with respect to $A$. $$\...
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1answer
82 views

Is there an optimization scheme/algorithm that converges, for this non-convex scenario but with some special properties

I have a smooth function $f(x) = \frac{g(x)}{h(x)}$ that is the ratio of two smooth convex functions $g(x)$ and $h(x)$. It is known that $f(x)$ has a global minimum, achieved at the unique point $x_0$....
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45 views

Simplification of an optimization objective

Let $G(V,E)$ is a weighted simple graph, where $V$ and $E$ are the set of vertices and Edges. The graph is undirected. Let $A \in \{0,1\}^{n\times n}$ and $W \in R_+^{n\times n}$ be the adjacency ...
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1answer
32 views

Vehicle Route assignment with capacity constraint

Problem Background I'm trying to find a solution/model to the following problem: Let's consider a cellular network (mobile network, ie., hexagonal cells) denoted $N$ composed of $|N|$ cells. Each ...
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1answer
183 views

Lack of quadratic convergence in Newton's method

It is well-known that Newton's method can converge quadratically, if initial guess is close enough and if the arising linear systems are solved accurately. I am applying Newton's method to highly ill-...
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1answer
71 views

Minimize a function with sparse Hessian

The problem I am trying to solve involves minimising a function with respect to a large number (probably 10,000+) of parameters. I can cheaply compute both its Jacobian and its Hessian. The Hessian is ...
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32 views

In-exact line search

In my class notes, the author says: "If $f:\mathbb{R}^n \to \mathbb{R}$ is bounded below and $p_k$ is a descent direction and the $\alpha-\beta$ also known as Armijo-Goldstein condition is met then ...
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30 views

Is this a form of stochastic gradient descent?

I want to minimize the following with respect to parameters $B$. $$\sum_{k = 1}^{K} f(A_{k}, B)$$ where $A_k$ are $K$ different data-sets and $B$ is a matrix of parameters. Can I do this by a ...
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1answer
23 views

Gradient ascent method with a constant step size?

I'm trying to use the gradient ascent method on a convex function like the multivariate-Normal density function with respect to its parameters (the original is a bit more complicated), something ...
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2answers
183 views

Best software to do big number calculations quickly

I am trying to do some work on some math conjecture. I am testing the conjecture numbers using very large math numbers (100+ digits ). I am currently using python to test these numbers. In the ...
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1answer
47 views

Question about strange outputs from the CVXPY solver

I am familiarizing myself with CVXPY, and encountered a strange problem. I have the following simple toy optimization problem: ...
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58 views

Given a list of intervals, find region that is contained by the largest number of those intervals

Start with 1d case. Say I have lots of 1d intervals $[s_i, e_i]$ and I want to find an interval $[s^*, e^*]$ to maximise the count of interval $i$ such that $[s_i, e_i]\supseteq [s^*, e^*]$. 1d case ...
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71 views

Nonlinear least squares and regularization

Consider the nonlinear least-squares minimization of a vector of $n$ residuals $\mathbf{f}$ in $p$ parameters $\mathbf{x}$: $$ \min_{\mathbf{x}} || \mathbf{f}(\mathbf{x}) ||^2 $$ This can be done with ...
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1answer
64 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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1answer
65 views

Minimize cost with Levenberg-Marquart method

I want to minimize a cost function of the form, $$ \min_{q,t}\left(q^T\left(\mathcal A + \mathcal B\right)q + t^T\mathcal C t+\delta t+\varepsilon Q(q)^TW(q)t+\lambda\left(1-q^Tq\right)^2\right) $$ ...
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1answer
60 views

How to derive the optimal bayesian solution to a model of two normal distributed populations

In the "Introduction" section of the paper Support-Vector Networks, it mentioned Fisher's solution to a model of two normal distributed populations: My questions are: How to derive equation (1)? I ...
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1answer
59 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)? ...
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2answers
114 views

Method to find PDE equation coefficient satisfying mean solution?

What is the best approach to go about solving a PDE problem of the type \begin{equation} k^3\Delta u - k(\mathbf{1}\cdot\nabla u) = 0\, ,\\ u=g\; \text{on}\; \Gamma_D\, ,\\ mean(u) = u_\text{...
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1answer
50 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, \...
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1answer
68 views

Compute affine transformation between two sets of points

Consider two sets of points $P = (P_1, ...,P_n), \ Q = (Q_1, ..., Q_m) $ included in $\mathbb{R}^3$. I'm looking to compute an optimal affine transformation that "maps" $Q$ to $P$, although the sets ...
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1answer
70 views

Nonlinear least squares when some parameters are linear

Consider the least squares problem, $$ \min_{\mathbf{a},\mathbf{b}} || \mathbf{f}(\mathbf{a},\mathbf{b})||^2 $$ where $\mathbf{a},\mathbf{b}$ represent the unknown parameters to be found. In my ...
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0answers
351 views

Good C++ optimization library for BFGS

To implement maximum likelihood estimators, I am looking for a good C++ optimization library that plays nicely with Eigen's matrix objects. Eigen has some capability of interfacing of its own but if ...
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1answer
47 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 ...
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25 views

Where can I find sample data for large linear programming optimization problems?

I am doing a comparison of different algebraic modeling langues (AMPL, AIMMS, GAMS, Pyomo) in both theoretical and practical terms. As a practical experiment I am trying to measure problem model ...
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1answer
82 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 ...
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46 views

Order of a principal term

In Yurii Nesterov's Introductory Lectures on Convex Optimization, there is a bound for the total number of iterations for some process. See page 109: $$\left[\frac{1}{\ln(2(1-\kappa))} \ln\frac{t_0-t^...
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32 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 ...
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48 views

Nonlinear functional optimization in radial coordinates

I am currently implementing classical density functional theory for a radially symmetric system. In mathematical terms, I am searching for a function $f(r)$ that minimizes a functional $\Omega[f]$. ...
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76 views

How to numerically optimize affine transformations?

I need to optimize affine transformations for of a set of triangles using energy function based on the connectivity. The energy of an edge $e_j$ between triangles $T_a, T_b$ is given by $$ E_j = \...
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1answer
26 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 ...
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0answers
104 views

Difference between Dishonest Newton method and Very Dishonest Newton method

What is the difference between the Dishonest Newton method and the Very Dishonest Newton method? Is there a difference or do they mean the same thing? I have tried searching for this on the internet ...
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33 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))...
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1answer
61 views

Convergence rate and complexity for convex minimization problem

In Yurii Nesterov's Introductory Lectures on Convex Optimization, there is a description of the rate of convergence and corresponding upper bound for the analytical complexity of a minimization ...
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1answer
32 views

Log-transformation of decision variables in parameter estimation

I am trying to find the diffusion coefficient ($D$) and the partition coefficient ($KLP$) using experimental data of desorption of a pollutant from a film into a liquid. This process can be modelled, ...
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32 views

Stochastic conjugate directions to improve convergence in narrow valleys

My question concerns a specific statement in this paper: N. N. Schraudolph and T. Graepel, "Conjugate Directions for Stochastic Gradient Descent," in Int. Conf. Artificial Neural Networks, Berlin, ...
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1answer
388 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 ...
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1answer
38 views

What is a good way to select a small subset (say 50) of items from a large pool of items (say 5 million) while minimizing an objective function?

I have 5 million items that have 10 features (all continuous and not categorical) each and would like to select a small subset of these items. Ideally, I want to manually specify 10 features of my own ...
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1answer
81 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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1answer
67 views

What is a “good enough” method of assigning values to n variables subject to basic bounding constraints while maintaining relative weights?

Given triples of $n$ floating point values $$(\min_1, \max_1, w_1), \dots, (\min_n, \max_n, w_n)$$ and a value $V$, what is a good algorithhm to assign values $v_i$ to each of the triples such that ...