# The Johnson-Lindenstrauss Lemma

The so-called “curse of dimensionality” reflects the idea that many methods are more difficult in higher-dimensions. This difficulty may be due a number of issues that become more complicated in higher-dimensions: e.g., NP-hardness, sample complexity, or algorithmic efficiency. Thus, for such problems, the data are often first transformed using some dimensionality reduction technique before applying the method of choice. In this post, we discuss a result in high-dimensional geometry regarding how much one can reduce the dimension while still preserving $\ell_2$ distances.

## Johnson-Lindenstrauss Lemma

Given $N$ points $$z_1,z_2,\ldots,z_N \in \mathbb{R}^d$$, we want to find $N$ points $$u_1,\ldots,u_N \in \mathbb{R}^k$$, where $k \ll d$, such that the distance between points is approximately preserved, i.e., for all $i,j$, $||z_i - z_j||_2 \leq ||u_i - u_j||_2 \leq (1+\epsilon) ||z_i-z_j||_2,$ where $||z||_2 := \sqrt{\sum_l |z_{l}|^2}$ is the $\ell_2$ norm. Thus, we’d like to find some mapping $f$, where $u_i = f(z_i)$, that maps the data to a much lower dimension while satisfying the above inequalities.

The Johnson-Lindenstrauss Lemma (JL lemma) tells us that we need dimension $k = O\left(\frac{\log N}{\epsilon^2}\right)$, and that the mapping $f$ is a (random) linear mapping. The proof of this lemma is essentially given by constructing $u_i$ via a random projection, and then showing that for all $i,j$, the random variable $||u_i - u_j||$ concentrates around its expectation.

This argument is an example of the probabilistic method, which is a nonconstructive method of proving existence of entities with particular properties: if the probability of getting an entity with the desired property is positive, then this entity must be an element of the sample space and therefore exists.

### Proof of the JL lemma

We can randomly choose $k$ vectors $(x_n)_{n=1}^k$, where each $x_n \in \mathbb{R}^d$, by choosing each coordinate $x_{nl}$ of the vector $x_n$ randomly from the set $\left\{\left(\frac{1+\epsilon}{k}\right)^{\frac{1}{2}},-\left(\frac{1+\epsilon}{k}\right)^{\frac{1}{2}}\right\}.$

Now consider the mapping from $\mathbb{R}^d \rightarrow \mathbb{R}^k$ defined by the inner products of $z \in \mathbb{R}^d$ with the $k$ random vectors: $z \mapsto (z^\top x_1, \ldots, z^\top x_k)$ So, each vector $u_i = (z_i^\top x_1,\ldots, z_i^\top x_k)$ is obtained via a random projection. (Alternatively, we can think of the mapping as a random linear transformation given by a random matrix $A \in \mathbb{R}^{k \times d}$, where the $k$ vectors form the rows of the matrix, and $Az_i = u_i$.) The goal is to show that there exists some $u_1,\ldots,u_k$ that satisfies the above inequalities.

Fixing $i,j$, define $u := u_i - u_j$ and $z := z_i - z_j$, and $Y_n := \left(\sum_{l=1}^d z_l x_{nl} \right)^2$. Thus, we can write the squared $\ell_2$ norm of $u$ as % where $x_{nl}$ refers to the $l$th component of the $n$th vector.

Now we consider the random variable $Y_n$ in the sum. The expectation is $\mathbb{E}(Y_n) = \frac{1+\epsilon}{k} ||z||_2^2.$ So, the expectation of $$||u||_2^2$$ is

It remains to show that $||u||_2^2$ concentrates around its mean $\mu$, which we can do using a Chernoff bound. In particular, consider the two cases of $||u||_2^2 > (1 + \delta) \mu$ and $% $. Via a Chernoff bound, the probability of at least one of these two “bad” events occurring is upper bounded by $\Pr[\{ ||u||^2 > (1+\delta) \mu)\} \lor \{||u||^2 > (1-\delta) \mu \}] < \exp(-c \delta^2 k),$ for some $c > 0$.

Recall that $||u|| := ||u_i - u_j||$, and so there are $\binom{N}{2}$ such random variables. Now choosing $\delta = \frac{\epsilon}{2}$, the probability that any of these random variables is outside of $(1 \pm \frac{\epsilon}{2})$ of their expected value is bounded by

which follows from a union bound.

Choosing $k > \frac{8(\log N + \log c)}{\epsilon^2}$ ensures that with all $\binom{N}{2}$ variables are within $(1 \pm \frac{\epsilon}{2})$ of their expectations, i.e., $(1+\epsilon) ||z||_2^2$. Thus, rewriting this, we have that for all $i,j$,

which then implies the desired result:

### References

1. Arora and Kothari. High Dimensional Geometry, Curse of Dimensionality, Dimension Reduction.
2. Dasgupta and Gupta. An Elementary Proof of Theorem of Johnson and Lindenstrauss.
3. Nelson. Dimensionality Reduction Notes Part 1 Part 2

# References on Bayesian nonparametrics

Last updated: 11/15/17

This post is a collection of references for Bayesian nonparametrics that I’ve found helpful or wish that I had known about earlier. For many of these topics, some of the best references are lecture notes, tutorials, and lecture videos. For general references, I’ve prioritized listing newer references with a more updated or comprehensive treatment of the topics. Many references are missing, and so I’ll continue to update this over time.

# Nonparametric Bayes: an introduction

These are references for people who have little to no exposure to the area, and want to learn more. Tutorials and lecture videos offer a great way to get up to speed on some of the more-established models, methods, and results.

1. Teh (and others). Nonparametric Bayes tutorials.

This webpage lists a number of review articles and lecture videos that as an introduction to the topic. The main focus is on Dirichlet processes, but a few tutorials cover other topics: e.g., Indian buffet processes, fragmentation-coagulation processes.

2. Broderick. Nonparametric Bayesian Methods: Models, Algorithms, and Applications. 2017. See also joint tutorial with Michael Jordan at the Simon’s Institute.

An introduction to Dirichlet processes and the Chinese restaurant process, and nonparametric mixture models. Also comes with R code for generating from and sampling (inference) in these models.

3. Wasserman and Lafferty. Nonparametric Bayesian Methods. 2010.

This gives an overview of Dirichlet processes and Gaussian processes and places these methods in the context of popular frequentist nonparametric estimation methods for, e.g., CDF and density estimation, regression function estimation.

# Theory and foundations

It turns out there’s a lot of theory and foundational topics.

1. Orbanz. Lecture notes on Bayesian nonparametrics. 2014.

A great introduction to foundations of Bayesian nonparametrics, and provides many references for those who want a more in-depth understanding of topics. E.g.: random measures, clustering and feature modeling, Gaussian processes, exchangeability, posterior distributions.

2. Ghosal and van der Vaart. Fundamentals of Bayesian Nonparametric Inference. Cambridge Series in Statistical and Probabilistic Mathematics, 2017. contents

The most recent textbook on Bayesian nonparametrics, focusing on topics such as random measures, consistency, contraction rates, and also covers topics such as Gaussian processes, Dirichlet processes, beta processes.

3. Kleijn, van der Vaart, van Zanten. Lectures on Nonparametric Bayesian Statistics. 2012.

Lecture notes with some similar topics as (and partly based on) the Ghosal and van der Vaart textbook, including a comprehensive treatment of posterior consistency.

## Specific topics

1. Kingman. Poisson processes. Oxford Studies in Probability, 1993.

Everything you wanted to know about Poisson processes. (See Orbanz lecture notes above for more references on even more topics on general point process theory.)

2. Pitman. Combinatorial stochastic processes. 2002.

3. Aldous. Exchangeability and related topics. 1985.

4. Orbanz and Roy. Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. IEEE TPAMI, 2015.

5. Broderick, Jordan, Pitman. Cluster and feature modeling from combinatorial stochastic processes. 2013.

## Background: probability

Having basic familiarity with measure-theoretic probability is fairly important for understanding many of the ideas in this section. Many of the introductory references aim to avoid measure theory (especially for the discrete models), but even this is not always the case, so it is helpful to have as much exposure as possible.

1. Hoff. Measure and probability. Lecture notes. 2013. pdf

Gives an overview of the basics of measure-theoretic probability, which are often assumed in many of the above/below references.

2. Williams. Probability with martingales. Cambridge Mathematical Textbooks, 1991.

3. Cinlar. Probability and stochastics. Springer Graduate Texts in Mathematics. 2011.

# Models and inference algorithms

There are too many papers on nonparametric Bayesian models and inference methods. Below, I’ll list a few “classic” ones, and continue to add more over time. The above tutorials contain many more references.

## Dirichlet processes: mixture models and admixtures

### Dirichlet process mixture model

1. Neal. Markov Chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 2000. pdf

2. Blei and Jordan. Variational inference for Dirichlet process mixtures. Bayesian Analysis, 2006. pdf

### Hierarchical Dirichlet process

1. Teh, Jordan, Beal, Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 2006. pdf

2. Hoffman, Blei, Wang, Paisley. Stochastic variational inference. Journal of Machine Learning Research, 2013. pdf

### Nested Dirichlet process & Chinese restaurant process

1. Rodriguez, Dunson, Gelfand. The Nested Dirichlet process. Journal of the American Statistical Association, 2008. pdf

2. Blei, Griffiths, Jordan. The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 2010. pdf

3. Paisley, Wang, Blei, Jordan. Nested hierarchical Dirichlet processes. IEEE TPAMI, 2015. pdf

### Mixtures of Dirichlet processes

1. Antoniak. Mixture of Dirichlet processes with applications to Bayesian nonparametrics. Annals of Statistics, 1974. pdf

1. Ishwaran and James. Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 2001. pdf

2. MacEachern. Estimating normal means with a conjugate style dirichlet process prior. Communications in Statistics - Simulation and Computation, 1994. pdf

3. Escobar and West. Bayesian density estimation and inference using mixtures. Journal of the American Statististical Association, 1995. pdf

## Indian buffet processes and latent feature models

1. Griffiths and Ghahramani. The Indian buffet process: an introduction and review. Journal of Machine Learning Research, 2011. pdf

2. Thibaux and Jordan. Hierarchical beta processes and the Indian Buffet process. AISTATS, 2007. pdf | longer

# Online decision making under total uncertainty

In this post, we will discuss a few simple algorithms for online decision making with expert advice. In particular, this setting assumes no prior distribution on the set of outcomes, but we use hindsight to improve future decisions. The algorithms discussed include a simple deterministic and randomized majority weighted decision algorithm.

Lastly, we discuss a randomized algorithm called the multiplicative weights algorithm. This algorithm has been discovered in a number of fields, and is the basis of many popular algorithms, such as the Ada Boost algorithm in machine learning and game-playing algorithms in economics.

This post mostly follows [1] and [2], which contain much more detail on this subject. In particular, we will omit proofs and refer to these references for the details.

# Overview

Consider a setting with $% $ rounds. During each round, you get a finite set of actions you can take, e.g., $\mathcal{A} = \{0,1\}$, and associated with each action is some cost associated with it, that is revealed after taking the action. We would like to design a policy that minimizes our cost (or maximizes the reward).

For example: consider the scenario of predicting whether or not a single stock’s price will go up or down. Thus, each round is a day, and the action we take is binary, corresponding to up/down. At the end of the day, we observe the final price of the stock: if we make a correct prediction, we lose nothing, but if we make an incorrect prediction, we lose 1 dollar.

We will consider the setting of total uncertainty, where we a priori have no knowledge of the distribution on the set of outcomes, e.g., due to lack of computational resources or data.

We will consider a few algorithms based on knowledge of $n$ experts.

Consider again the example of predicting a stock’s price, whose movement can be arbitrary or adversarial (which comes up, in practice, in a variety of other settings). But, we get to view the predictions of $n$ experts (who may or may not be good at predicting and could even be correlated in some manner).

We would like to design an algorithm that limiting the total cost – i.e., bad predictions – by limiting it to be about the same as the best expert. Because we do not know who the best expert is until the end, we need some way of maintaining and updating our belief of the best expert so that we can make some prediction in each round.

## Predicting with the majority

### Deterministic algorithm

The simplest algorithm is to just predict according to the majority prediction of the experts: if most experts predict the price will go up, we will also predict the price will go up. But what happens if the majority is wrong every single day? Then, we will lose money every day.

Instead, we can maintain a weight for each expert $w_i$, that is initially 1 for all experts, but that we decay every time the expert makes a mistake in the prediction. Then, our action is to predict according to the weighted majority, which will downweight the predictions of the bad experts. Thus, the algorithm will predict, at each round, according to the decision with the highest total weight of the experts.

Let $\eta \in (0,1)$ be a parameter such that if the expert makes a mistake, we will decay their weight by $(1-\eta)$, i.e., for the $i$th expert, we have for the $t$th round

Then, after $T$ steps, if $m_i^{(T)}$ is the number of mistakes from expert $i$, we have following bound on the number of mistakes of our algorithm $M^{(T)}$: for all $i$, we have

Note that the best expert will have the fewest number of mistakes $m_i^{(T)}$, and that the bound holds for all experts. Thus, the number of mistakes the algorithm makes is roughly a little less than twice the number of mistakes of the best expert (only the first term depends on $T$).

### Randomized algorithm

It turns out we can do even better if we convert the above algorithm to a randomized algorithm. Here, instead of predicting with the weighted majority, we will predict with the weighted majority with probability proportional to the weight. For instance, if the total weight of the experts predicting “up” is $\frac{3}{4}$, then instead of predicting up, our algorithm will instead predict up with probability proportional to $\frac{3}{4}$.

For this algorithm, we instead have the bound

which is a factor of 2 less (in the first term) than the above deterministic algorithm. Thus, this algorithm will perform roughly on the same order as the best expert.

# Multiplicative weights

Now, we consider a more general setting. Here we will choose one decision in each round out of $n$ possible decisions, and each decision will incur a cost, which is revealed after making the decision. Above, we studied the special case where each decision corresponds to a choice of an expert, and the cost $m_i^{(t)}$ is 1 for a mistake, and 0 otherwise. Here we will instead consister costs that can be in $[-1,1]$.

A naive strategy would be to pick a decision randomly; the expected penalty is that of the average decision. But, if a few decisions are better, we can observe this as the costs are revealed, and upweight those better decisions so that we can pick them in the future. This motivates the multiplicative weight algorithm, which has been discovered independently in many fields, e.g., machine learning, optimization, and game theory. The goal is to design an algorithm that, in the long run, has total expected cost roughly on the order of the best decision, i.e., $\min_i \sum_{t=1}^T m_i^{(t)}$.

Again, we will maintain a weighting of the decisions $w_i^{(t)}$, where all weights initially are set to 1. At each round $t=1,\ldots,T$, we have a distribution $p^{(t)}$ over a set of decisions $p^{(t)} = \left\{\frac{w_1^{(t)}}{\Phi^{(t)}}, \ldots, \frac{w_n^{(t)}}{\Phi^{(t)}} \right\},$ where $\Phi^{(t)} = \sum_i w_i^{(t)}$ is the normalization term.

For each round $t=1,\ldots,T$, we iterate the following:

1. Randomly select a decision $i$ from $p^{(t)}$ (thus, each decision is chosen with probability proportional to its weight $w_i^{(t)}$).

2. The decision is made, and the cost vector $m^{(t)}$ is revealed, where the $i$th component correponds to the cost of decision $i$ (with cost $m_i^{(t)} \in [-1,1]$). The costs could be chosen arbitrarily by nature.

3. Update the weights of the costly decisions: for each decision $i$, set $w_i^{(t+1)} = w_i^{(t)} (1 - \eta \, m_i^{(t)}),$ where $\eta \leq \frac{1}{2}$ is fixed in advance. Here, the multiplicative term $(1-\eta m_i^{(t)})$ is less than 1 (thus, a decay) if there is a larger cost, but if the cost is negative, this will increase the weights. A cost of 0 would not change the weight at all.

The expected cost of the algorithm from sampling decision $i \sim p^{(t)}$ is $E_{i \in p^{(t)}}(m_i^{(t)}) = \langle m^{(t)}, p^{(t)} \rangle,$ i.e., the sum of the costs weighted by the probability of sampling each respective decision. The total expected cost is the sum of the expected cost for each round: $\sum_{t=1}^T \langle m^{(t)}, p^{(t)}\rangle.$ We can now consider a bound for this value.

## Bound for the expected total cost

Assuming all costs $m_i^{(t)}$ lie in $[-1,1]$, and that $\eta \leq 1/2$, then we have the following bound after $T$ rounds: for any decision $i$,

$\sum_{t=1}^T \langle m^{(t)}, p^{(t)}\rangle \leq \sum_{t=1}^T m_i^{(t)} + \eta \sum_{t=1}^T |m_i^{(t)}| + \frac{\log n}{\eta}.$

# References

2. Arora, Hazan, Kale. The multiplicative weights update method: a meta algorithm and its applications.
3. Borodin and El Yaniv. Online computation and competitive analysis.

# Wavelets and adaptive data analysis

For data that have a high signal-to-noise ratio, a nonparametric, adaptive method might be appropriate. In particular, we may want to fit the data to functions that are spatially imhomogenous, i.e., the smoothness of the function $f(x)$ varies a lot with $x$.

In this post, we will discuss wavelets, which can be used an adaptive nonparametric estimation method. First, we will introduce some background on function spaces and Fourier transforms, and then we will discuss Haar wavelets, a specific type of wavelet, and how to construct wavelets in general. This presentation follows Wasserman [2], but I’ve included some additional code and images.

# Preliminaries

## Function spaces

Let $$L_2(a,b)$$ denote the set of functions $$f : [a,b] \rightarrow \mathbb{R}$$ such that $\int_a^b f^2(x) dx < \infty.$ For our purposes, we will assume $$a=0,b=1$$. The inner product of $$f,g \in L_2(a,b)$$ is defined as $\langle f,g \rangle := \int_a^b f(x) g(x) dx,$ and the norm of $$f$$ is defined as $|| f || = \left( \int_a^b f^2(x) dx \right)^{\frac{1}{2}}.$ A sequence of functions $$\phi_1,\phi_2,\ldots$$ is orthonormal if $$||\phi_j|| = 1$$ for all $$j$$ (i.e., has norm 1), and $$\int_a^b \phi_i(x) \phi_j(x) dx = 0, \,\, i \neq j$$ (i.e., orthogonal).

A complete (i.e., the only function orthogonal to each $$\phi_j$$ is the 0 function) and orthonormal set of functions form a basis, i.e., if $$f \in L_2(a,b)$$, then $$f$$ can be expanded in the basis in the following way: $f(x) = \sum_{j=1}^\infty \theta_j \phi_j(x),$ where $$\theta_j = \int_a^b f(x) \phi_j(x) dx$$.

## Sparse functions and Fourier transforms

A function $$f = \sum_j \beta_j \phi_j$$ is sparse in a basis $$\phi_1,\phi_2,\ldots$$ if most of the $$\beta_j$$’s are 0 or close to 0. Sparseness can be seen as a generalization of smoothness, i.e., a smooth function is sparse, but there are also nonsmooth functions that are sparse.

Let $$f^{*}$$ denote the Fourier transform of a function $$f$$: $f^{*}(t) = \int_{-\infty}^\infty \exp(-ixt) \, f(x) \,dx.$ We can recover $$f$$ at almost all $$x$$ using the inverse Fourier transform: $f(x) = \frac{1}{2\pi} \int_{-\infty}^\infty \exp(ixt) \, f^*(t) \,dt,$ assuming that $$f^{*}$$ is absolutely integrable.

Throughout our discussion of wavelets, we will use the following notation: given any function $$f$$ and $$j,k \in \mathbb{Z}$$, define $f_{jk}(x) = 2^{\frac{j}{2}} \, f(2^j x - k).$

# Wavelets

We now turn our attention to wavelets, beginning with the simplest type of wavelet, the Haar wavelet.

## Haar wavelet

Haar wavelets are a simple type of wavelet given in terms of step-functions. Specifically, these wavelets are expressed in terms of the the Father and Mother Haar wavelets. Our goal is to define an orthonormal basis for $L_2([0,1])$—to do so, we need to introduce the Father and Mother wavelets and their shifted and rescaled sets.

The Father wavelet is defined as:

and looks like:

The Mother wavelet is defined as:

and looks like:

Now we define the wavelets as shifted and rescaled versions of the Father and Mother wavelets, as above:

and

Below we plot some examples.

The shifted/rescaled father wavelet $\phi_{2,2}$ looks like:

The shifted/rescaled mother wavelet $\psi_{2,2}$ looks like:

Now we define the set of rescaled and shifted mother wavelets at resolution $j$ is defined as: $W_j = \{\psi_{jk}, k=0,1,\ldots,2^{j-1}\}.$

We plot an example where $j=3$:

The next theorem defines gives an orthonormal basis for $L_2(0,1)$ in terms of the introduced sets, which allows us to write any function in this space as a linear combination of the basis elements.

Theorem: The set of functions $\{\phi, W_0, W_2, W_2,\ldots\}$ is an orthonormal basis for $L_2(0,1)$, i.e., the set of real-valued functions on $[0,1]$ where $% $.

As a result, we can expand any function $f \in L_2(0,1)$ in this basis:

where $\alpha = \int_0^1 \phi(x) dx$ is the scaling coefficient, and $\beta_{jk} = \int_0^1 f(x) \psi_{jk}(x) dx$ are the detail coefficients.

So to approximate a function $f \in L_2(0,1)$, we can take the finite sum

This is called the resolution $J$ approximation, and has $2^J$ terms.

We consider an example below. Suppose we are interested in approximating the Doppler function:

We can approximate this function by considering several resolutions (i.e., finite truncations of the wavelet expansion sum). Below, we plot the original function along with the resolution $J=3,5,8$ approximations:

Here, the coefficients were computed using numerical quadrature. Below, we plot the resolution $J=5$ approximation along with the coefficients. The $y$-axis represents which resolution or level the coefficient comes from. The height of the bars are proportional to the size of the coefficients, and the direction of the bar corresponds to the sign of the coefficient.

For instance, for certain applications, the $x$-axis could be represent time, and the resolutions could then be interpreted as sub-intervals of time.

## Constructing smooth wavelets

Haar wavelets are simple to describe and are localized, i.e., the mass is concentrated in one area. We can express these same ideas for more general functions, which can give us approximations that are smooth and localized. Intuitively, it is useful to consider these specific concepts in terms of Haar wavelets, and to know that we can use these ideas for more general functions $\phi$ in the following way.

Given any function $\phi$, we can define the subspaces of $L_2(\mathbb{R})$ as follows:

Definition: We say that $\phi$ generates a multiresolution analysis (MRA) of $\mathbb{R}$ if

and

i.e., for any function $f \in L_2(\mathbb{R})$, there exists a sequence of functions $f_1, f_2, \ldots$ such that each $f_r \in \bigcup_{j \geq 0} V_j$ and $||f_r - f|| \rightarrow 0$ as $r \rightarrow \infty$.

In other words, (…)

Lemma: If $V_0, V_1, V_2, \ldots$ is an MRA generated by $\phi$, then

is an orthonormal basis for $V_j$.

As an example, we consider the father Haar wavelet as the function $\phi$. The the MRA generated by $\phi$ is given by $\{\phi, V_0, V_1,\ldots\}$, where each $V_j$ is the set of functions $f \in L_2(\mathbb{R})$ that are piecewise constant on the interval

for $k \in \mathbb{Z}$.

# Code

Code (Jupyter/iPython notebook) for generating these plots is available on Github.

# References

1. W. Härdle, G. Kerkyacharian, D. Picard, A. Tsybakov. Wavelets, Approximation, and Statistical Applications.
2. L. Wasserman. All of Nonparametric Statistics. Chapter 9.

# Linear programming (LP), LP relaxations, and rounding

In this post, we’ll review linear systems and linear programming. We’ll then focus on how to use LP relaxations to provide approximate solutions to other (binary integer) problems that are NP-hard. Much of this post follows these randomized algorithms course notes [1].

# Linear programming

One of the most basic ways of thinking of a problem is using linear equations. It turns out that linear problems are also easy to solve, whereas nonlinear problems are often hard to solve.

We can express a system of linear equations in matrix form as follows. Let $$A \in \mathbb{R}^{m \times n}$$ be the matrix of coefficients, $$x \in \mathbb{R}^n$$ a vector of variables (that we are interested in), $$b \in \mathbb{R}^m$$ a vector of real numbers. We can express a system of $m$ equations and $n$ variables as the matrix equation $Ax=b$.

We can also consider a system of linear inequalities by replacing some of the equalities with inequalities; i.e., $Ax \geq b$, where $\geq$ is a component-wise inequality. (Throughout this post, we will overload the $\geq$ symbol and other inequalities to denote both component-wise inequality and scalar inequalities). The set of solutions is called the feasible region and is a convex polytope. In the figure below, the edges of the convex polytope are the linear inequalities, and any point in this feasible region satisfies this set of inequalities.

(Image source: EMT6680)

We are often interested in optimizing (e.g., minimizing or maximizing) some linear function subject to a set of linear inequalities (i.e., constraints). This is called a linear program (LP) and in its most general form can be expressed as: $\text{minimize } c^\top x$ $\text{subject to } Ax \geq b, x\geq 0.$ Here $c,b$ are known vectors of coefficients, $A$ is a known matrix of coeffcients, and $x$ is the unknown vector of the variables of interest.

(Fun history fact: linear programming was invented in 1939 by the Russian mathematician Leonid Kantorovich to optimize the organization of industrial production and allocation of a society’s resources.)

The optimal value of the objective function (that satisfies the constraints) $$x^*$$ will be some vertex/corner/extreme point of the convex feasible region. The simplex algorithm is a method to enuerate the vertices one by one, decreasing the objective function at every step.

# LP relaxations and approximation algorithms

In a number of problems, the unknown variables are required to be integers, which defines an integer program. Unlike linear programs, which can be solved efficiently, integer programs, while being more practical, are NP-hard. However, what we can do is relax these integer programs so that they are linear programs, by turning the integer constraints into linear inequalities.

Here we will look at three examples of LP relaxations. The first is an example that even though we solve the relaxed LP, we still end up with the integer solution. The other examples require some approximation achieved by rounding.

## The assignment problem

We are interested in assigning $n$ jobs to $n$ factories, each job having some cost (raw materials, etc.). Let $c_{ij}$ dnote the cost of assigning job $i$ to factory $j$, and let $x_{ij}$ denote the assignment of job $i$ to factory $j$. Thus $x_{ij}$ is a binary-valued variable and corresponds to a binary integer program.

To write the relaxed LP program, we turn this binary constraint into an inequality: $x_{ij} \geq 0 \text{ and } x_{ij} \leq 1,$ for all $i,j \in [n] = \{1,\ldots,n\}$. But we also want each job to one factory and each factory to contain one job. So, we add the constraint $\sum_{j=1}^n x_{ij}=1$ for each job $i \in [n]$ to guarantee that each job $i$ is only assigned to one factory, and the constraint $\sum_{i=1}^n x_{ij}=1$ for all $j \in [n]$ to ensure that each factory only gets a single job. This results in the relaxed LP program $\text{minimize } \sum_{ij \in [n]} c_{ij} x_{ij}$ $\text{subject to } 0 \leq x_{ij} \leq 1, \quad i,j \in [n]$ $\sum_{j=1}^n x_{ij}=1, \quad i \in [n]$ $\sum_{i=1}^n x_{ij}=1, \quad j \in [n]$ This LP results in variables that are all binary and actually solves the assignment problem.

However, in general, the solution of the LP produces a fractional solution, instead of an integer. Thus, to solve the original integer program, we can round the fractional solution to get an integer solution. We now discuss several examples where the LP relaxation gives us a fractional solution and how we might round these solutions (e.g., deterministically with a threshold or random rounding).

## Approximate rounding solutions

### Deterministic rounding

The simplest way to round a fractional solution to a binary one is by picking some threshold, e.g., 1/2, and rounding up or down depending on if variable is greater than or less than the threshold. We now consider the weighed vertex cover problem, which is NP-hard, and show that this deterministic rounding procedure gives us a good enough” approximate solution; i.e., with high probability, we get something within $(1+\epsilon)$ of the true value.

#### LP relaxation for the weighted vertex cover problem

In the weighted vertex cover problem, we are given a graph $G = (V,E)$ and some weight $w_i \geq 0$ for each vertex $i$. A vertex cover is a subset of $S \subset V$ such that every edge $e \in E$ has at least one vertex $v \in S$. The optimization problem is: find a vertex cover with minimum total weight. The relaxed LP program is: $\text{minimize } f(x) = w^\top x$ $\text{subject to } 0 \leq x \leq 1$ $\quad x_i + x_j \geq 1, \quad\forall \{i,j\} \in E$ The first line is the objective, i.e., the total weight, where $x_i$ is a variable that represents whether vertex $i$ is in the cover $S$. The second line gives the relaxed inequality, and the last line guarantees that we have a vertex cover, i.e., that for every each edge $$\{i,j\} \in E$$, we have at least one vertex that is in the cover (either $x_i=1$ and/or $x_j=1$).

#### How good is deterministic rounding?

Suppose $O$ is the optimal value of this program, and let $V$ be the minimum weight of the vertex cover problem. We have that $O \leq V$ since every binary integer solution is also a fractional solution.

Now apply deterministic rounding to get a new set $S$, where every vertex $i$ with $x_i \geq \frac{1}{2}$ is in the set, and otherwise not in $S$. This produces a vertex cover because for every edge $$\{i,j\} \in E$$, we know that we must satisfy the constraint $x_i + x_j \geq 1,$ which implies that $x_i \geq \frac{1}{2}$ and\or $x_j \geq \frac{1}{2}$.

Since the optimal value at the solution is $O = \sum_{i=1}^n w_i x_i,$ when we apply the rounding, we only pick the vertices that are greater than $\frac{1}{2}$, and so the resulting weight of $S$ is at most twice the weight of the optimal value of the LP, i.e., $2O$. Thus, we have a resulting vertex cover with cost within a factor of 2 of the optimum cost.

### Randomized rounding

Instead of the deterministic thresholding procedure, we could consider a randomized rounding procedure. One simple randomized rounding procedure is round the fractional variable $x_i$ up to 1 with probability $x_i$ (and down with probability $1-x_i$). The rounded variable therefore has expectation $x_i$, and by linearity, the expectation of a linear constraint (i.e., $c^\top x = d$) that the fractional $x$ satisfies is, in expectation, satisfied by the rounded variable.

We will consider using this randomized rounding procedure for the MAX-2SAT problem. In this problem, we have $n$ boolean variables $x_1,\ldots,x_n$, and a set of clauses of two literals $y \vee z$, where each literal is either some $x_i$ or its negation $\neg x_i$. The goal is to find an assignment that maximizes the number of satisfied clauses $y \vee z$ (i.e., the clauses is true).

## LP relaxation for MAX-2SAT

For this problem, we will form the following LP relaxation. Let $J$ be a set of clauses, and $y_j = (y_{j1}, y_{j2})$ the literals in clause $j$; and so $y_{j1}$ is $x_i$ if the first literal in clause $j$ is $x_i$, and $1-x_i$ if it is the negation.

We will define the variable $z_j$ for each clause $j$, where %

Thus, taking the sum over all $z_j$ gives us the total number of satisfied clauses, and thus defines our objective function.

So, we can express this as the relaxed LP

where $\mathbf{1}:=[1,1]^\top$. Here $x$ and $z$ are both relaxed to be fractional, and the last line ensures that the logic of the clauses holds.

#### How good is randomized rounding?

Now let $O$ be the optimal value of the this LP, and $M$ denote the number of clauses satisfied by the best assignment in MAX-2SAT. We have that $$M \leq O$$.

Now apply randomized rounding to the fractional solution to get the 0/1 assignment. It turns out that the expected number of clauses satisfied is at least $\frac{3}{4}$ the optimal value of the LP $O$.

We can have $j$ clauses that are either of size 1 or size 2. If the clause is of size 1, $x_r$, then the probability it is satisfied is $x_r$. But we must have $x_r \geq z_j$ from the constrainst, so the probability this clause is satisfied is at least $\frac{3}{4}z_j$.

Now suppose we have a clause of size 2, e.g., $x_r \vee x_s$. Note that the probability of success after randomized rounding is 1 minus the product of if both are 0, i.e., $1 - (1-x_r)(1-x_s) = x_s + x_r - x_r x_s,$ but since we know from the constraint that $z_j \leq x_r + x_s,$ we can write that the probability of success is at least %

So, since all clauses individually are satisfied with probability at least $\frac{3}{4}z_j$, by linearity of expectation, the expected number of clauses satisfied is at least $\frac{3}{4} O$.

## Summary

Here we have discussed approximate solutions to integer programs via LP relaxations and two simple rounding schemes. Other problems likely require more complicated rounding schemes. Here we discussed two problems where simple deterministic and random rounding give easy to analyze approximate solutions are are within a constant factor of the solution. Many more rounding schemes and algorithms can be found in Williamson and Shmoys (2010) [2].

## Resources

1. Arora, Kothari, Weinberg (2017). Advanced Algorithm Design Notes

2. Williamson and Shmoys (2010). Design of Approximation Algorithms