In one of the problem (3.49(c)) of Convex Optimization book (By Boyd and Vandenberghe) it is asked to show that product over sum function $$g(x)=\frac{\prod_{i=1}^nx_i}{\sum_{i=1}^nx_i}$$ is log concave. Now $f(x)=\log(g(x))$ is given as follows $$f(x)=\sum_{i=1}^n\log(x_i)-\log(\sum_{i=1}^nx_i).$$ Now if $g(x)$ is log concave then $f(x+tv)$ should be concave for all values of $t$ which means that the double derivative of $f(x+tv)$ with respect to $t$ should be negative for all values of $t$. The double derivative of $f(x+tv)$ with respect to $t$ is given as follows $$f''(x+tv)=-\sum_{i=1}^n\frac{v_i^2}{(x_i+tv_i)^2}+\frac{(\sum_{i=1}^nv_i)^2}{(\sum_{i=1}^nx_i+t\sum_{i=1}^nv_i)^2}.$$ After this the solution manual proves that $f''(x+tv)<0$ for $t=0$. I do not know why they only check for $t=0$ case and ignore all the other values of $t$. Please help me in understanding this part. Thanks in advance.
2026-03-28 05:38:46.1774676326
How to show that product over sum function is log concave?
1.2k Views Asked by Bumbble Comm https://math.techqa.club/user/bumbble-comm/detail At
1
There are 1 best solutions below
Related Questions in OPTIMIZATION
- Optimization - If the sum of objective functions are similar, will sum of argmax's be similar
- optimization with strict inequality of variables
- Gradient of Cost Function To Find Matrix Factorization
- Calculation of distance of a point from a curve
- Find all local maxima and minima of $x^2+y^2$ subject to the constraint $x^2+2y=6$. Does $x^2+y^2$ have a global max/min on the same constraint?
- What does it mean to dualize a constraint in the context of Lagrangian relaxation?
- Modified conjugate gradient method to minimise quadratic functional restricted to positive solutions
- Building the model for a Linear Programming Problem
- Maximize the function
- Transform LMI problem into different SDP form
Related Questions in CONVEX-ANALYSIS
- Proving that: $||x|^{s/2}-|y|^{s/2}|\le 2|x-y|^{s/2}$
- Convex open sets of $\Bbb R^m$: are they MORE than connected by polygonal paths parallel to the axis?
- Show that this function is concave?
- In resticted domain , Applying the Cauchy-Schwarz's inequality
- Area covered by convex polygon centered at vertices of the unit square
- How does positive (semi)definiteness help with showing convexity of quadratic forms?
- Why does one of the following constraints define a convex set while another defines a non-convex set?
- Concave function - proof
- Sufficient condition for strict minimality in infinite-dimensional spaces
- compact convex sets
Related Questions in CONVEX-OPTIMIZATION
- Optimization - If the sum of objective functions are similar, will sum of argmax's be similar
- Least Absolute Deviation (LAD) Line Fitting / Regression
- Check if $\phi$ is convex
- Transform LMI problem into different SDP form
- Can a linear matrix inequality constraint transform to second-order cone constraint(s)?
- Optimality conditions - necessary vs sufficient
- Minimization of a convex quadratic form
- Prove that the objective function of K-means is non convex
- How to solve a linear program without any given data?
- Distance between a point $x \in \mathbb R^2$ and $x_1^2+x_2^2 \le 4$
Trending Questions
- Induction on the number of equations
- How to convince a math teacher of this simple and obvious fact?
- Find $E[XY|Y+Z=1 ]$
- Refuting the Anti-Cantor Cranks
- What are imaginary numbers?
- Determine the adjoint of $\tilde Q(x)$ for $\tilde Q(x)u:=(Qu)(x)$ where $Q:U→L^2(Ω,ℝ^d$ is a Hilbert-Schmidt operator and $U$ is a Hilbert space
- Why does this innovative method of subtraction from a third grader always work?
- How do we know that the number $1$ is not equal to the number $-1$?
- What are the Implications of having VΩ as a model for a theory?
- Defining a Galois Field based on primitive element versus polynomial?
- Can't find the relationship between two columns of numbers. Please Help
- Is computer science a branch of mathematics?
- Is there a bijection of $\mathbb{R}^n$ with itself such that the forward map is connected but the inverse is not?
- Identification of a quadrilateral as a trapezoid, rectangle, or square
- Generator of inertia group in function field extension
Popular # Hahtags
second-order-logic
numerical-methods
puzzle
logic
probability
number-theory
winding-number
real-analysis
integration
calculus
complex-analysis
sequences-and-series
proof-writing
set-theory
functions
homotopy-theory
elementary-number-theory
ordinary-differential-equations
circles
derivatives
game-theory
definite-integrals
elementary-set-theory
limits
multivariable-calculus
geometry
algebraic-number-theory
proof-verification
partial-derivative
algebra-precalculus
Popular Questions
- What is the integral of 1/x?
- How many squares actually ARE in this picture? Is this a trick question with no right answer?
- Is a matrix multiplied with its transpose something special?
- What is the difference between independent and mutually exclusive events?
- Visually stunning math concepts which are easy to explain
- taylor series of $\ln(1+x)$?
- How to tell if a set of vectors spans a space?
- Calculus question taking derivative to find horizontal tangent line
- How to determine if a function is one-to-one?
- Determine if vectors are linearly independent
- What does it mean to have a determinant equal to zero?
- Is this Batman equation for real?
- How to find perpendicular vector to another vector?
- How to find mean and median from histogram
- How many sides does a circle have?
At any given point $x$, they are taking an aribitrary $1$-dimensional slice of the domain through $x$: the line of points of the form $x + tv$, where $t$ ranges over $\mathbb{R}$. They then show that the second derivative is negative, at that point, along any arbitrary line. They have to let $t$ range freely initially, so that the function is defined along the line, at least in a neighbourhood of $x$, so that differentiation is meaningful.
They don't have to show it for other values of $t$, because they've already shown this for arbitrary $x$. If you want to know whether this holds true for $x + t_0 v$, for some fixed $t_0 \in \mathbb{R} \setminus \lbrace 0 \rbrace$, then simply redefine $x_0 = x + t_0 v$, consider the function $t \mapsto f(x_0 + tv)$, and do all the above steps, including considering where $t = 0$.
What this means is that every $1$-dimensional slice of the function is log-concave. Since concavity is defined along line segments, this means the entire function is log-concave.