A non-linear optimization problem includes an objective function (to be minimized or maximized) and some number of equality and/or inequality constraints where the objective or some of the constraints are non-linear. Use this tag for questions related to the theory of solving such problems or for ...

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3
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1answer
176 views

fantasy basketball model

i'm creating a fantasy basketball model (could be used in other games too) where we can project how well a player will do against another team even when the player hasn't played against a certain team ...
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1answer
64 views

Solving a Non-linear Multivariable System of equations

How would I go about solving a system of nonlinear equations where the highest degree is two? For example: $$f_1(x) = f_1(x_1, x_2,\dots, x_n) = 0,$$ $$f_2(x) = f_2(x_1, x_2,\dots, x_n) = 0,$$ ...
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1answer
43 views

Quadratic Program reformulation

I have the quadratic program $$\max\quad \mu^Tx+r_fx_0-\gamma \sum\limits_{i=1}^n |x_i-y_i|-\frac{\lambda}{2}x^TVx$$ $$\text{s.t. }\quad \mathbb{1}^Tx+x_0=1$$ where $\mu$, $r_f$, $\gamma$, $\lambda$, ...
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0answers
29 views

Newton's method: Is the change of parameter values between consecutive steps always decreasing?

Assume that I have a twice differentiable function $f(x)$ which I try to maximize with respect to $x$ (let's say $x$ is $k$-dimensional vector). When performing optimization via Newton algorithm, ...
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0answers
28 views

Are there any algorithms for simultaneous optimization of multiple objective functions?

I would like to minimize a set of similar objective functions $$f_\boldsymbol{s}(\boldsymbol{x}),$$ where $\boldsymbol{x} \in A \subseteq \mathbb{R}^M$ and the parameterization $\boldsymbol{s} \in S ...
0
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1answer
55 views

Roots of an equation using Maple

I am using Maple to find the roots of a non-linear equation in one variable. When I solve the equation, I get only 2 negative roots whereas if I plot the graph of the function, it also shows that the ...
2
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0answers
181 views

Direct multiple shooting (numerical optimal control)

please, Iam currently implementing direct multiple shooting method* and I need one simple but fundamental concept answered: When I want to provide not only objective funtion value (result of ODE ...
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0answers
63 views

SOCP or SDP optimization problem

I am studying an optimization problem \begin{equation} \mathbf{w}^* = \text{argmax} \sum_{d=1}^D \log \bigg( \frac{|\mathbf{f}_d^H\mathbf{w}|^2+c_1}{|\mathbf{f}_d^H\mathbf{w}|^2+c_2} \bigg)\\ \\ ...
3
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2answers
43 views

Sensitivity of polynomial global minimizers with respect to perturbations in the coefficients.

I'm trying to find the value of a global minimizers of a multivariate polynomial (4 variables) of high order numerically. The numerical values of the coefficients are coming from noisy measurements ...
0
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0answers
50 views

Newton's method for unconstrained optimization applied to a quartic function in R2

I am faced with the task of applying Newton's method to the following problem: $$ \text{min} ~~~~~ 8x_1x_2+\frac{1}{4}(x_1-x_2)^4 $$ where $x \in \mathbb{R}^2$. For clarification, the Newton method ...
2
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1answer
18 views

About constrained optimization

I've the following optimization problem:$$\min f(\theta_1,\theta_2)=\frac{a}{\cos\theta_1\cdot v_1}+\frac{b}{\cos\theta_2\cdot v_2}$$$$\operatorname{sub}\quad a\cdot\tan\theta_1+b\cdot\tan\theta_2=c$$ ...
2
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1answer
96 views

Why is the conjugate direction better than the negative of gradient, when minimizing a function

In gradient descent we minimize a function $f(\textbf{x})$, by using the update rule: $$\textbf{x}_{t+1} = \textbf{x}_t-\alpha\nabla f(\textbf{x}_t).$$ We also know, that at each iteration we have ...
1
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1answer
22 views

A weird optimization problem

I've the following optimization problem:$$\max f(R,z)=R^2(a+z)$$$$\operatorname{sub}\begin{cases}R^2+z^2=a^2\\0\le z \le a\end{cases}$$ Once solved it gives $z=a/3$, ... Consider now the ...
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0answers
51 views

Can a positive definite kernel produce a kernel matrix which has negative eigenvalues?

(1) I've read that a symmetric matrix is positive definite when its associated eigenvalues are all positive. I am learning SVM lately, and have come to know a $d$th-degree polynomial kernel ...
0
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1answer
95 views

What is the correct change of variables to yield convexity in this nonlinear optimization problem?

$$ \text{min. } x/y \\ \text{s.t. } 2\leq x \leq 3 \\ x^2+y/z\leq \sqrt{y} \\ x/y=z^2 \\ x,y,z\geq 0 $$ To transform this problem into a nonlinear convex optimization problem, both the objective ...
1
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1answer
49 views

Why test problems in convex optimization are mostly random?

Very often people who compare performance of different algorithms in convex optimization use randomly generated data. For instance, this often happens in compressed sensing and signal processing. Is ...
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0answers
54 views

Solve: tanh(x) = a*x + b - most efficient way

I work on DSP code, where some equations are of form: tanh(x) = a*x + b (tanh or other hyperbolic functions) Currently I use Newton-Raphson method. Is there a better/faster method of finding ...
0
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1answer
46 views

How to linearize this mixed-integer nonlinear constraint

Can someone please help me to linearize the following nonlinear/nonconvex constraint: $\sum\limits_{n=1}^Na_n\rm{log_2}(1+x_ny_n)\le M\delta$ Here $a_n \in\{0,1\}$, binary integer variable $0\le ...
0
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1answer
48 views

How to linearise this nonlinear constraint

I want to linearize or convexify this following constraint. Here $c_t$ is binary integer variables, $p_t$ are continuous variable which are bounded. $\gamma$ is a continuous variable. $h_t$ and $V$ ...
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0answers
30 views

What is a good optimization algorithm/tool for otimization on Partially Ordered set?

Actually I'm interested to minimize following kind of functions: $f: U \rightarrow V$ where: $U$ is a vector space and $V$ is a Ordered vector space, i mean Partially Ordered Vector space. ...
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0answers
35 views

Solving large non-linear polynomial equation system

I have a 2 order equation system of 7 unknowns. It is constructed as this: F1=0,F2=0,F3=0...F7=0 of which F1=f1*f2,F2=f3*f4... And f1=a1*p1+a2*p2+a3*p3+a4*p4+a5*p5+a6*p6+a7*p7 a1~a7 are known ...
0
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1answer
46 views

Non linear programming

Could you please help me in solving the problem posted below. A company uses a raw material to produce two types of products. When processed, each unit of raw material yields 2 units of product 1 and ...
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0answers
32 views

Linear independence of equality constraint gradients in constraint qualifications

I'm, trying to get an intuitive feel for the various constraint qualifications for KKT points. Most of them seem to rely on the linear independence of $\nabla g_i(x^*)$ where $g_i$ are the equality ...
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0answers
31 views

Reduce degree of a high degree unconstrained binary term to quadratic unconstrained binary term

I'm working on a optimization project, in this project I have to convert higher order unconstrained binary polynomial to quadratic unconstrained binary polynomial. Can anyone give me a hint of how to ...
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0answers
56 views

Mixed-Integer Linear Programing : get the maximum constant associated to a non null variable

Does anyone know a way get the maximum constant associated to a non null variable using Mixed-Integer Linear Programing ? I would like to get the variable $a$ in this description : $$ i = 1,\ldots,m ...
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0answers
103 views

Line search Armijo, Wolfe, Strong Wolfe and Goldstein.

What are the articles (References) who proposed the line search of Armijo, Wolfe, Strong Wolfe, and Goldstein? Articles precursors of unidirectional searches?
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1answer
166 views

Absolute extrema of $\sin x+\cos y+\sin (xy)$ on a square

I want to find the absolute extrema of the function $f(x,y)=\sin x+\cos y+\sin (xy)$ on $\{ (x,y) \mid 0\le x\le 2\pi,~0\le y \le 2\pi \}$. I tried by finding the gradient of the function $f$, but it ...
2
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0answers
34 views

Exercice the converge $r$-superlinearly

Give $x_0 \in \mathbb{R}^*$. Show that $\{x_k\} \subset \mathbb{R}$ converge $r$-superlinearly for $x^∗=0$, where $x_k$ is defined by $x_{k+1}=(1−\beta_k)x_k$ and $\beta_k=1−2^{-k}$ if $k=i^2$ for ...
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0answers
74 views

Proving/deciding concavity of a function of two variables

I would like to formally prove that the function $f(x,y) = \frac{(c+1)e^{-x}(xe^{x+y}+y)}{(c+2)(e^{x+y}-1)+e^y} $ is concave ($ c>2$ is a constant, and both $x,\, y \in \mathbf{R_+}$). Plots of ...
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0answers
42 views

gradient descent - cost reduces and then increases

I am optimizing a function using Gradient Descent. The learning rate is fixed. First for few iterations the cost decreases after that it starts increases. What is the reason for this?
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0answers
47 views

optimization of nasty expression with nice symmetry between expressions

Consider the function $\ f(x,y,z,\rho_a,\rho_b)=$ $ \log \left(1+ (x+ \rho_ay)^2 + \frac{(z+ \rho_by)^2}{1+(x+ \rho_by)^2} \right)+ \log\left(1+ (x+ \rho_by)^2 + \frac{(z+ \rho_ay)^2}{1+(x+ ...
4
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1answer
153 views

Lipschitz continuity of parametric optimizer

Consider the parametric optimal solution $x^{*}: \mathbb{R}^n \rightarrow \mathbb{R}^n$ defined as $$ x^*( y ) := \arg\min_{x \in X } \ \ x^\top x + x^\top A y \\ \quad \qquad \text{subject to: } \ ...
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0answers
38 views

When result of max of min problem is equal to min of max problem

Let's assume there are two functions $f(x)$ and $g(x)$. I want to know when the optimal $x$ of max of min of $f(x)$ and $g(x)$ is not equal to optimal $x$ of min of max of $\frac{1}{f(x)}$ and ...
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2answers
49 views

Is the set of all projection matrices a convex set?

The set $\phi=\{P| P^2=P\}$ contains all projection matrix. Is this set $\phi$ convex?
0
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1answer
33 views

Solving coupled non-linear equations

I am struggling to understand what the following question requires me to do: I believe I need to differentiate implicitly, but am unsure how I show it cannot be done.
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37 views

Explain KKT conditions without reference to duality.

Is it possible to explain (not derive) KKT necessary conditions without reference to the concept of Lagrangian duality?
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36 views

Constrained non-linear optimization problem

For some background, this comes from a sample size allocation problem in statistics. I am trying to minimize the following function (a sum of three variances), and could use some help with direction ...
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0answers
67 views

Saddle point problem (KKT) with block-diagonal matrix

Consider the following saddle point problem originating from an interior-point method algorithm: $$ \begin{bmatrix}\mathbf{H} & \mathbf{A}^{T}\\ \mathbf{A} & \mathbf{0} ...
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2answers
502 views

Maximizing curious symmetric function from simple combinatorics

A curious symmetric function crossed my way in some quantum mechanics calculations, and I'm interested its maximum value (for which I do have a conjecture). (This question has been posted at ...
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0answers
27 views

Non linear Programming Problem

I am struggling with the following question: Solve the following programing problem: max $f(x_1,x_2)$= $ \sqrt{(x_1 + 1) (x_2+1)} $ subject to $x_2-(x_1-1)^2 \leq 0 $; $x_1+x_2 \leq 7 $; $x_1, x_2 ...
0
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0answers
28 views

Separate a list of spheres into several lists, each contained in a sphere with a radius no larger than specified.

I have a list of arbitrary spheres, what I want to end up with is that list separated into a number of groups, where spheres in each group all fit into thier specific larger sphere. The limitation is, ...
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119 views

Fenchel dual vs Lagrange dual

Consider the Fenchel dual and the Lagrangian dual. Are these duals equivalent? In other words, is using one of the these duals (say for solving an optimization), would give the same answer as using ...
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1answer
70 views

Question regarding KKT conditions in optimization

Following is Proposition 3.3.7 in Bersekas' Nonlinear Programming. Let $x^*$ be the local minimum of the problem: $$\text{Minimize }\; f(x) $$ $$ \text{subject to: }\ h_j(x) = 0, ...
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0answers
105 views

Consider the problem minimize $f(x)= x^4 −1. $

I am studying for a test and I found this problem in the textbook that I'm using, there may be a conceptual problem that I'm coming across. My test is on unconstrained optimization (multi-variable) ...
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0answers
59 views

Solving many independent non-linear systems simultaneously

I'm working on solving lots of systems of nonlinear equations. Luckily, the non-linear equation is the same, but the parameters are different: $$ f(\vec{x}_0; c_0) = 0\\ f(\vec{x}_1; c_1) = 0\\ ...
2
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1answer
125 views

min : sum of L2 norm and squared-L2 norm.

Is there a closed-form solution of the following convex problem: $$\min_x \| x - u \| + C \| x - v \|^2$$ where $\| \cdot \|$ is the L2 norm.
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2answers
30 views

How to mathematically prove the optimality conditions for a univariate function?

Consider a univariate function $f(x)$. I know the graphical intuition behind why $f'(x)=0$ at the extrema of $f$. But how do you prove it mathematically? I start with the assumption of $x^*$ being a ...
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1answer
56 views

Why don't we go beyond the Hessian in multivariate optimization?

In univariate optimization, we perform the first derivative test to identify stationary points and the second derivative test to classify the stationary points as minima, maxima and inconclusive. When ...
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1answer
95 views

Constrained Optimization : Minimize sum of dot products

I am working on a problem to minimize sum of dot product. The problem can be stated as following. Given a matrix where each element is either 0 or 1. $$ \ A_{ij} = \{0,1\}; $$ with the constraint ...
2
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0answers
75 views

Constrained non-linear Optimization using Newton's method - Portfolio optimization

I want to solve following constrained optimization problem from portfolio optimization: The solution is supposed to be a modified risk parity portfolio: The optimization problem is: \begin{align} ...