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This thesis is primarily focused on problems in extremal combinatorics, although we will also consider some questions of analytic and algorithmic nature. The d-dimensional hypercube is the graph with vertex set {0,1}d where two vertices are adjacent if they differ in exactly one coordinate. In Chapter 2 we obtain an upper bound on the 'saturation number' of Qm in Qd. Specifically, we show that for m ≥ 2 fixed and d large there exists a subgraph G of Qd of bounded average degree such that G does not contain a copy of Qm but, for every G' such that G ⊊ G' ⊆ Qd, the graph G' contains a copy of Qm. This result answers a question of Johnson and Pinto and is best possible up to a factor of O(m). In Chapter 3, we show that there exists ε > 0 such that for all k and for n sufficiently large there is a collection of at most 2(1-ε)k subsets of [n] which does not contain a chain of length k+1 under inclusion and is maximal subject to this property. This disproves a conjecture of Gerbner, Keszegh, Lemons, Palmer, Pálvölgyi and Patkós. We also prove that there exists a constant c ∈ (0,1) such that the smallest such collection is of cardinality 2(1+o(1))ck for all k. In Chapter 4, we obtain an exact expression for the 'weak saturation number' of Qm in Qd. That is, we determine the minimum number of edges in a spanning subgraph G of Qd such that the edges of E(Qd)\E(G) can be added to G, one edge at a time, such that each new edge completes a copy of Qm. This answers another question of Johnson and Pinto. We also obtain a more general result for the weak saturation of 'axis aligned' copies of a multidimensional grid in a larger grid. In the r-neighbour bootstrap process, one begins with a set A0 of 'infected' vertices in a graph G and, at each step, a 'healthy' vertex becomes infected if it has at least r infected neighbours. If every vertex of G is eventually infected, then we say that A0 percolates. In Chapter 5, we apply ideas from weak saturation to prove that, for fixed r ≥ 2, every percolating set in Qd has cardinality at least (1+o(1))(d choose r-1)/r. This confirms a conjecture of Balogh and Bollobás and is asymptotically best possible. In addition, we determine the minimum cardinality exactly in the case r=3 (the minimum cardinality in the case r=2 was already known). In Chapter 6, we provide a framework for proving lower bounds on the number of comparable pairs in a subset S of a partially ordered set (poset) of prescribed size. We apply this framework to obtain an explicit bound of this type for the poset 𝒱(q,n) consisting of all subspaces of 𝔽qnordered by inclusion which is best possible when S is not too large. In Chapter 7, we apply the result from Chapter 6 along with the recently developed 'container method,' to obtain an upper bound on the number of antichains in 𝒱(q,n) and a bound on the size of the largest antichain in a p-random subset of 𝒱(q,n) which holds with high probability for p in a certain range. In Chapter 8, we construct a 'finitely forcible graphon' W for which there exists a sequence (εi)∞i=1 tending to zero such that, for all i ≥ 1, every weak εi-regular partition of W has at least exp(εi-2/25log∗εi-2) parts. This result shows that the structure of a finitely forcible graphon can be much more complex than was anticipated in a paper of Lovász and Szegedy. For positive integers p,q with p/q ❘≥ 2, a circular (p,q)-colouring of a graph G is a mapping V(G) → ℤp such that any two adjacent vertices are mapped to elements of ℤp at distance at least q from one another. The reconfiguration problem for circular colourings asks, given two (p,q)-colourings f and g of G, is it possible to transform f into g by recolouring one vertex at a time so that every intermediate mapping is a p,q-colouring? In Chapter 9, we show that this question can be answered in polynomial time for 2 ≤ p/q < 4 and is PSPACE-complete for p/q ≥ 4.

In mathematical finance, the sensitivities of option prices to various market parameters, also known as the “Greeks”, reflect the exposure to different sources of risk. Computing these is essential to predict the impact of market moves on portfolios and to hedge them adequately. This is commonly done using Monte Carlo simulations. However, obtaining accurate estimates of the Greeks can be computationally costly. Multilevel Monte Carlo offers complexity improvements over standard Monte Carlo techniques. However the idea has never been used for the computation of Greeks. In this work we answer the following questions: can multilevel Monte Carlo be useful in this setting? If so, how can we construct efficient estimators? Finally, what computational savings can we expect from these new estimators? We develop multilevel Monte Carlo estimators for the Greeks of a range of options: European options with Lipschitz payoffs (e.g. call options), European options with discontinuous payoffs (e.g. digital options), Asian options, barrier options and lookback options. Special care is taken to construct efficient estimators for non-smooth and exotic payoffs. We obtain numerical results that demonstrate the computational benefits of our algorithms. We discuss the issues of convergence of pathwise sensitivities estimators. We show rigorously that the differentiation of common discretisation schemes for Ito processes does result in satisfactory estimators of the the exact solutions’ sensitivities. We also prove that pathwise sensitivities estimators can be used under some regularity conditions to compute the Greeks of options whose underlying asset’s price is modelled as an Ito process. We present several important results on the moments of the solutions of stochastic differential equations and their discretisations as well as the principles of the so-called “extreme path analysis”. We use these to develop a rigorous analysis of the complexity of the multilevel Monte Carlo Greeks estimators constructed earlier. The resulting complexity bounds appear to be sharp and prove that our multilevel algorithms are more efficient than those derived from standard Monte Carlo.

This thesis examines graph polynomials and particularly their complexity. We give short proofs of two results from Gessel and Sagan (1996) which present new evaluations of the Tutte polynomial concerning orientations. A theorem of Massey et al (1997) gives an expression concerning the average size of a forest in a graph. We generalise this result to any simplicial complex. We answer a question posed by Kleinschmidt and Onn (1995) by showing that the language of partitionable simplicial complexes is in NP. We prove the following result concerning the complexity of the Tutte polynomial: Theorem 1. For any fixed k, there exists a polynomial time algorithm A, which will input any graph G, with tree-width at most k, and rational numbers x and y, and evaluate the Tutte polynomial, T(G;x,y). The rank generating function S of a graphic 2-polymatroid was introduced by Oxley and Whittle (1993). It has many similarities to the Tutte polynomial and we prove the following results. Theorem 2. Evaluating S at a fixed point (u,v) is #P-hard unless uv=1 when there is a polynomial time algorithm. Theorem 3. For any fixed k, there exists a polynomial time algorithm A, which will input any graph G, with tree-width at most k, and rational numbers u and v, and evaluate S(G;u,v). We consider a class of graphs $S$, which are those graphs which are obtainable from a graph with no edges using the unsigned version of Reidemeister moves. We examine the relationship between this class and other similarly defined classes such as the delta-wye graphs. There remain many open questions such as whether S contains every graph. However we have an invariant of the moves, based on the Tutte polynomial, which allows us to determine from which graph with no edges, if any, a particular graph can be obtained. Finally we consider a new polynomial on weighted graphs which is motivated by the study of weight systems on chord diagrams. We give three states model and a recipe theorem. An unweighted version of this polynomial is shown to contain as specialisations, a wide range of graph invariants, such as the Tutte polynomial, the polymatroid polynomial of Oxley and Whittle (1993) and the symmetric function generalisation of the chromatic polynomial introduced by Stanley (1995). We close with a discussion of complexity issues proving hardness results for very restricted classes of graphs.

In this dissertation, we study two problems that have the theme of extracting information from lower dimensional samples. A number of recent works have studied algorithms for entrywise $\ell_p$-\emph{low rank approximation}, namely algorithms which given an $n \times d$ matrix $A$ (with $n \geq d$), output a rank-$k$ matrix $B$ minimizing $\|A-B\|_p^p=\sum_{i,j} |A_{i,j} - B_{i,j}|^p$ when $p > 0$; and $\|A-B\|_0 = \sum_{i,j} [A_{i,j} \neq B_{i,j}]$ for $p=0$, where $\|A-B\|_0$ denotes the number of entries $(i,j)$ for which $A_{i,j} \neq B_{i,j}$.For $p = 1$, this is often considered more robust than the SVD, while for $p = 0$ this corresponds to minimizing the number of disagreements, or robust PCA. This problem is known to be NP-hard for $p \in \{0,1\}$, already for $k = 1$, and while there are polynomial time approximation algorithms, their approximation factor is at best $\poly(k)$. It was left open if there was a polynomial-time approximation scheme (PTAS) for $\ell_p$-approximation for any $p \geq 0$. We show the following:\begin{enumerate} \item On the algorithmic side, for $p \in (0,2)$, we use a technique called \emph{sketching} to give the first $n^{\poly(k/\eps)}$ time $(1+\eps)$-approximation algorithm. For $p = 0$, there are various problem formulations, a common one being the binary setting for which $A\in\{0,1\}^{n\times d}$ and $B = U \cdot V$, where $U\in\{0,1\}^{n \times k}$ and $V\in\{0,1\}^{k \times d}$. For this setting, we obtain an algorithm with time $n \cdot d^{\poly(k/\eps)}$. \item On the hardness front, for $p \in (1,2)$, we show under the Small Set Expansion Hypothesis and Exponential Time Hypothesis (ETH), there is no constant factor approximation algorithm running in time $2^{k^{\delta}}$ for a constant $\delta > 0$, showing an exponential dependence on $k$ is necessary. We also show for finite fields of constant size, under the ETH, that any fixed constant factor approximation algorithm requires $2^{k^{\delta}}$ time for a constant $\delta > 0$.\end{enumerate} \emph{Population recovery} is the problem of learning an unknown distribution over an unknown set of $n$-bit strings, given access to independent \emph{traces} from the distribution that have been independently corrupted according to some noise channel. Recent work has intensively studied such problems both for the {bit-flip} noise channel and for the erasure noise channel. In this dissertation we initiate the study of population recovery under the \emph{deletion channel}, in which each bit $b$ is independently \emph{deleted} with some fixed probability and the surviving bits are concatenated and transmitted. This is a far more challenging noise model than bit-flip~noise or erasure noise; indeed, even the simplest case in which the population is of size 1 (corresponding to a trivial probability distribution supported on a single string) corresponds to the \emph{trace reconstruction} problem, which is a challenging problem that has received much recent attention. In this work we give algorithms and lower bounds for population recovery under the deletion channel when the population size is some value $\ell > 1$. As our main sample complexity upper bound, we show that for any population size $\ell = o(\log n / \log \log n)$, a population of $\ell$ strings from $\zo^n$ can be learned under deletion channel noise using $\smash{2^{n^{1/2 + o(1)}}}$ samples. On the lower bound side, we show that at least $n^{\Omega{(\ell)}}$ samples are required to perform population recovery under the deletion channel when the population size is $\ell$, for all $\ell \leq n^{1/2 - \epsilon}$.

At its core, cryptography relies on problems that are simple to construct but difficult to solve unless certain information (the “key”) is known. Many of these problems come from number theory and group theory. One method of obtaining groups from which to build cryptosystems is to define algebraic curves over finite fields and then derive a group structure from the set of points on those curves. This thesis serves as an exposition of Elliptic Curve Cryptography (ECC), preceded by a discussion of some basic cryptographic concepts and followed by a glance into one generalization of ECC: cryptosystems based on hyperelliptic curves.

Statistical inference on high-dimensional and noisy data is a central concern of modern computer science. Often, the main challenges are inherently computational: the problems are well understood from a purely statistical perspective, but key statistical primitives -- likelihood ratios, Bayes-optimal estimators, etc. -- are intractable to compute on large and high-dimensional data sets. We develop a unified approach to algorithm design for statistical inference based on the Sum of Squares method, a powerful tool for convex programming with low-degree polynomials, which generalizes linear programming and spectral algorithms. We use this approach to design algorithms for a range of high-dimensional statistical problems, improving on state-of-the-art provable guarantees in several well-studied settings: clustering data from high-dimensional mixture models, community detection in random graphs, and more. We also prove computational lower bounds for some statistical problems, including the long-studied planted clique problem. Our lower bounds provide new strong evidence for the existence of information-computation gaps -- that is, statistical problems which are solvable given infinite computational resources, but not by efficient algorithms. In particular, we prove new lower bounds against the powerful Sum of Squares hierarchy of semidefinite programs, via a new technique called "pseudocalibration". Because the Sum of Squares hierarchy has provable guarantees matching those of most known techniques in algorithm design for inference, our lower bounds strongly suggest that the problems we study are intractable for polynomial-time algorithms. At very least, improving existing algorithms for them would require a major breakthrough. We show that polynomial-size semidefinite programs from the Sum of Squares hierarchy cannot refute the existence of cliques of size much less than the square root of n in n-node random graphs. Additionally, we prove a lower bound for sparse principal component analysis (PCA), showing that subexponential-size Sum of Squares semidefinite programs are needed to improve on the provable guarantees of existing spectral algorithms for sparse PCA. Our approach to algorithms and lower bounds suggests a new method to chart the edge of algorithmic tractability for statistical inference. We propose a classification of Bayesian inference problems according to solvability by algorithms which compute only simple statistics of input data -- triangle counts of graphs, top eigenvalues of matrices, etc. Our classification accurately predicts suspected information-computation gaps for many well-studied problems, including planted clique, planted constraint satisfaction, community detection in stochastic block models, component analysis problems, and more. This type of classification is novel for inference problems, and represents the first approach to trace the information-computation gaps in of all these problems to the same underlying mathematical structure.

The Stable Marriage Problem (SMP) is concerned with the follow scenario: suppose we have two disjoint sets of agents—for example prospective students and colleges, medical residents and hospitals, or potential romantic partners—who wish to be matched into pairs. Each agent has preferences in the form of a ranking of her potential matches. How should we match agents based on their preferences?We say that a matching is "stable" if no unmatched pair of agents mutually prefer each other to their assigned partners. In their seminal work on the SMP, Gale and Shapley prove that a stable matching exists for any preferences. They further describe an efficient algorithm for finding a stable matching. In this dissertation, we consider the computational complexity of the SMP in the distributed setting, and the complexity of finding “almost stable” matchings. Highlights include (1) communication lower bounds for finding stable and almost stable matchings, (2) a distributed algorithm which finds an almost stable matching in poly-logarithmic time, and (3) hardness of approximation results for three dimensional analogues of the SMP.

Spin systems originated in statistical physics as tools for modeling phase transitions in magnets. However, they have since been used to model complex systems arising in several other fields of study (e.g., in Bayesian inference and in the theory of social networks), so that various computational problems associated with them have received much attention in the literature. A lot of progress in the study of these problems has relied upon ideas from statistical physics; one example of this interplay has been the discovery of tight connections between the computational complexity of these problems and the phenomenon of phase transitions that many spin systems exhibit. Conversely, algorithmic ideas have also helped in the study of the phase transition phenomenon.This thesis presents two lines of work that fit into this theme. The first considers the approximation of the partition function, a pivotal quantity associated with spin systems. Here, we obtain—in various settings—deterministic polynomial time algorithms for approximating the partition function in the so called uniqueness regime, and thus strengthen the algorithmic side of the tight correspondence between phase transitions and the computational complexity of the partition function.The second line of work is concerned with the complexity of exact computation of various natural mean observables of spin systems, e.g., the magnetization in the Ising model. We relate these questions to the location of the complex zeros of the partition function, which have been studied in statistical physics because of their connections to the existence of phase transitions, and have been the subject of various celebrated results (such as the Lee–Yang theorem and the Heilmann–Lieb theorem). By proving a novel extension of the Lee–Yang theorem, we show that the magnetization of the ferromagnetic Ising model is indeed as hard to compute as the partition function (i.e., #P–hard). We also obtain similar results for the monomer–dimer model.

This dissertation develops efficient methods for calculating the makespan of a perturbed job shop. All iterative scheduling algorithms require their performance measure, usually the makespan, to be calculated during every iteration. Therefore, this work can enhance the efficiency of many existing scheduling heuristics, e.g. Tabu Search, Genetic Algorithms, Simulated Annealing etc. This increased speed provides two major benefits. The first is the capability of searching a larger solution space, and second is the capability to find a better solution due to the extra time. The following is a list of major highlights of this dissertation. The dissertation extends the hierarchical block diagram model formulation and composition that was originally proposed by Imaev[2]. An algorithm is developed that reduces the complexity of calculating the makespan of the perturbed schedule of job shop with no recirculation from O(MNlogMN) to O(N^2), where M is the number of machines and N the number of parts. An efficient algorithm that calculates kleene star of a lower triangular matrix is presented. This algorithm has complexity of O((n^3)/6) which is 1/16th of the traditional approach. Finally, a novel pictorial methodology, called the SBA (Serial Block Addition), is developed to calculate the makespan of a perturbed job shop. A very efficient single perturbed machine scheduling algorithm, with complexity of O(N^2), is derived using the SBA method. The algorithm was tested on 10,000 randomly generated problems. The solutions provided by scheduling algorithm were 95.27% times, within a 3% deviation of the optimal solutions.

For finite polyomino regions, tileability by a pair of rectangles is NP-complete for all but trivial cases yet can be solved in quadratic time for simply connected regions. Through a series of reductions and improvements, we construct a set of 117 rectangles for which the tileability of simply connected regions is NP-complete.Tiling by dominoes in the plane is a matching problem, and thus can be solved in polynomial time. While the decision problem remains polynomial in higher dimensions, we prove that the counting problem becomes #P-complete. We also establish NP- and #P-completeness results for another generalization of domino tilings to higher dimensions.We show that tileability of cofinite regions is undecidable, even for some fixed set of tiles, but present an algorithm for solving the case where all tiles are rectangular. We also show that whether a finite region can be enlarged by tiles such that the resulting region becomes tileable is also undecidable. Moreover, the existence of a tileable rectangle by a set of polyominoes is also shown to be undecidable.It is not surprising that tiles can emulate other computational systems such as Turing machines and cellular automata. Some of these connections and consequences are explored and outlined. In particular, there are conjectures in the theory of cellular automata that, if proved, could lead to improvements of results in the theory of tilings.