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Toric varieties are algebraic varieties arising from elementary geometric and combinatorial objects such as convex polytopes in Euclidean space with vertices on lattice points. Since many algebraic geometry notions such as singularities, birational maps, cycles, homology, intersection theory, and Riemann-Roch translate into simple facts about polytopes, toric varieties provide a marvelous source of examples in algebraic geometry. In the other direction, general facts from algebraic geometry have implications for such polytopes, such as to the problem of the number of lattice points they contain. In spite of the fact that toric varieties are very special in the spectrum of all algebraic varieties, they provide a remarkably useful testing ground for general theories.The aim of this mini-course is to develop the foundations of the study of toric varieties, with examples, and describe some of these relations and applications. The text concludes with Stanley's theorem characterizing the numbers of simplicies in each dimension in a convex simplicial polytope. Although some general theorems are quoted without proof, the concrete interpretations via simplicial geometry should make the text accessible to beginners in algebraic geometry.

Algebraic geometry --- Differential geometry. Global analysis --- 512.7 --- Algebraic geometry. Commutative rings and algebras --- Toric varieties. --- 512.7 Algebraic geometry. Commutative rings and algebras --- Toric varieties --- Embeddings, Torus --- Torus embeddings --- Varieties, Toric --- Algebraic varieties --- Addition. --- Affine plane. --- Affine space. --- Affine variety. --- Alexander Grothendieck. --- Alexander duality. --- Algebraic curve. --- Algebraic group. --- Atiyah–Singer index theorem. --- Automorphism. --- Betti number. --- Big O notation. --- Characteristic class. --- Chern class. --- Chow group. --- Codimension. --- Cohomology. --- Combinatorics. --- Commutative property. --- Complete intersection. --- Convex polytope. --- Convex set. --- Coprime integers. --- Cotangent space. --- Dedekind sum. --- Dimension (vector space). --- Dimension. --- Direct proof. --- Discrete valuation ring. --- Discrete valuation. --- Disjoint union. --- Divisor (algebraic geometry). --- Divisor. --- Dual basis. --- Dual space. --- Equation. --- Equivalence class. --- Equivariant K-theory. --- Euler characteristic. --- Exact sequence. --- Explicit formula. --- Facet (geometry). --- Fundamental group. --- Graded ring. --- Grassmannian. --- H-vector. --- Hirzebruch surface. --- Hodge theory. --- Homogeneous coordinates. --- Homomorphism. --- Hypersurface. --- Intersection theory. --- Invertible matrix. --- Invertible sheaf. --- Isoperimetric inequality. --- Lattice (group). --- Leray spectral sequence. --- Limit point. --- Line bundle. --- Line segment. --- Linear subspace. --- Local ring. --- Mathematical induction. --- Mixed volume. --- Moduli space. --- Moment map. --- Monotonic function. --- Natural number. --- Newton polygon. --- Open set. --- Picard group. --- Pick's theorem. --- Polytope. --- Projective space. --- Quadric. --- Quotient space (topology). --- Regular sequence. --- Relative interior. --- Resolution of singularities. --- Restriction (mathematics). --- Resultant. --- Riemann–Roch theorem. --- Serre duality. --- Sign (mathematics). --- Simplex. --- Simplicial complex. --- Simultaneous equations. --- Spectral sequence. --- Subgroup. --- Subset. --- Summation. --- Surjective function. --- Tangent bundle. --- Theorem. --- Topology. --- Toric variety. --- Unit disk. --- Vector space. --- Weil conjecture. --- Zariski topology.

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Ramsey theory is a fast-growing area of combinatorics with deep connections to other fields of mathematics such as topological dynamics, ergodic theory, mathematical logic, and algebra. The area of Ramsey theory dealing with Ramsey-type phenomena in higher dimensions is particularly useful. Introduction to Ramsey Spaces presents in a systematic way a method for building higher-dimensional Ramsey spaces from basic one-dimensional principles. It is the first book-length treatment of this area of Ramsey theory, and emphasizes applications for related and surrounding fields of mathematics, such as set theory, combinatorics, real and functional analysis, and topology. In order to facilitate accessibility, the book gives the method in its axiomatic form with examples that cover many important parts of Ramsey theory both finite and infinite. An exciting new direction for combinatorics, this book will interest graduate students and researchers working in mathematical subdisciplines requiring the mastery and practice of high-dimensional Ramsey theory.

Algebraic spaces. --- Ramsey theory. --- Ramsey theory --- Algebraic spaces --- Mathematics --- Algebra --- Physical Sciences & Mathematics --- Spaces, Algebraic --- Geometry, Algebraic --- Combinatorial analysis --- Graph theory --- Analytic set. --- Axiom of choice. --- Baire category theorem. --- Baire space. --- Banach space. --- Bijection. --- Binary relation. --- Boolean prime ideal theorem. --- Borel equivalence relation. --- Borel measure. --- Borel set. --- C0. --- Cantor cube. --- Cantor set. --- Cantor space. --- Cardinality. --- Characteristic function (probability theory). --- Characterization (mathematics). --- Combinatorics. --- Compact space. --- Compactification (mathematics). --- Complete metric space. --- Completely metrizable space. --- Constructible universe. --- Continuous function (set theory). --- Continuous function. --- Corollary. --- Countable set. --- Counterexample. --- Decision problem. --- Dense set. --- Diagonalization. --- Dimension (vector space). --- Dimension. --- Discrete space. --- Disjoint sets. --- Dual space. --- Embedding. --- Equation. --- Equivalence relation. --- Existential quantification. --- Family of sets. --- Forcing (mathematics). --- Forcing (recursion theory). --- Gap theorem. --- Geometry. --- Ideal (ring theory). --- Infinite product. --- Lebesgue measure. --- Limit point. --- Lipschitz continuity. --- Mathematical induction. --- Mathematical problem. --- Mathematics. --- Metric space. --- Metrization theorem. --- Monotonic function. --- Natural number. --- Natural topology. --- Neighbourhood (mathematics). --- Null set. --- Open set. --- Order type. --- Partial function. --- Partially ordered set. --- Peano axioms. --- Point at infinity. --- Pointwise. --- Polish space. --- Probability measure. --- Product measure. --- Product topology. --- Property of Baire. --- Ramsey's theorem. --- Right inverse. --- Scalar multiplication. --- Schauder basis. --- Semigroup. --- Sequence. --- Sequential space. --- Set (mathematics). --- Set theory. --- Sperner family. --- Subsequence. --- Subset. --- Subspace topology. --- Support function. --- Symmetric difference. --- Theorem. --- Topological dynamics. --- Topological group. --- Topological space. --- Topology. --- Tree (data structure). --- Unit interval. --- Unit sphere. --- Variable (mathematics). --- Well-order. --- Zorn's lemma.

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This book provides an accessible and self-contained introduction to the theory of algebraic curves over a finite field, a subject that has been of fundamental importance to mathematics for many years and that has essential applications in areas such as finite geometry, number theory, error-correcting codes, and cryptology. Unlike other books, this one emphasizes the algebraic geometry rather than the function field approach to algebraic curves. The authors begin by developing the general theory of curves over any field, highlighting peculiarities occurring for positive characteristic and requiring of the reader only basic knowledge of algebra and geometry. The special properties that a curve over a finite field can have are then discussed. The geometrical theory of linear series is used to find estimates for the number of rational points on a curve, following the theory of Stöhr and Voloch. The approach of Hasse and Weil via zeta functions is explained, and then attention turns to more advanced results: a state-of-the-art introduction to maximal curves over finite fields is provided; a comprehensive account is given of the automorphism group of a curve; and some applications to coding theory and finite geometry are described. The book includes many examples and exercises. It is an indispensable resource for researchers and the ideal textbook for graduate students.

Curves, Algebraic. --- Finite fields (Algebra) --- Modular fields (Algebra) --- Algebra, Abstract --- Algebraic fields --- Galois theory --- Modules (Algebra) --- Algebraic curves --- Algebraic varieties --- Abelian group. --- Abelian variety. --- Affine plane. --- Affine space. --- Affine variety. --- Algebraic closure. --- Algebraic curve. --- Algebraic equation. --- Algebraic extension. --- Algebraic function. --- Algebraic geometry. --- Algebraic integer. --- Algebraic number field. --- Algebraic number theory. --- Algebraic number. --- Algebraic variety. --- Algebraically closed field. --- Applied mathematics. --- Automorphism. --- Birational invariant. --- Characteristic exponent. --- Classification theorem. --- Clifford's theorem. --- Combinatorics. --- Complex number. --- Computation. --- Cyclic group. --- Cyclotomic polynomial. --- Degeneracy (mathematics). --- Degenerate conic. --- Divisor (algebraic geometry). --- Divisor. --- Dual curve. --- Dual space. --- Elliptic curve. --- Equation. --- Fermat curve. --- Finite field. --- Finite geometry. --- Finite group. --- Formal power series. --- Function (mathematics). --- Function field. --- Fundamental theorem. --- Galois extension. --- Galois theory. --- Gauss map. --- General position. --- Generic point. --- Geometry. --- Homogeneous polynomial. --- Hurwitz's theorem. --- Hyperelliptic curve. --- Hyperplane. --- Identity matrix. --- Inequality (mathematics). --- Intersection number (graph theory). --- Intersection number. --- J-invariant. --- Line at infinity. --- Linear algebra. --- Linear map. --- Mathematical induction. --- Mathematics. --- Menelaus' theorem. --- Modular curve. --- Natural number. --- Number theory. --- Parity (mathematics). --- Permutation group. --- Plane curve. --- Point at infinity. --- Polar curve. --- Polygon. --- Polynomial. --- Power series. --- Prime number. --- Projective plane. --- Projective space. --- Quadratic transformation. --- Quadric. --- Resolution of singularities. --- Riemann hypothesis. --- Scalar multiplication. --- Scientific notation. --- Separable extension. --- Separable polynomial. --- Sign (mathematics). --- Singular point of a curve. --- Special case. --- Subgroup. --- Sylow theorems. --- System of linear equations. --- Tangent. --- Theorem. --- Transcendence degree. --- Upper and lower bounds. --- Valuation ring. --- Variable (mathematics). --- Vector space.

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Research on interior-point methods (IPMs) has dominated the field of mathematical programming for the last two decades. Two contrasting approaches in the analysis and implementation of IPMs are the so-called small-update and large-update methods, although, until now, there has been a notorious gap between the theory and practical performance of these two strategies. This book comes close to bridging that gap, presenting a new framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function. The authors deal with linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. The framework also covers large classes of linear complementarity problems and convex optimization. The algorithm considered can be interpreted as a path-following method or a potential reduction method. Starting from a primal-dual strictly feasible point, the algorithm chooses a search direction defined by some Newton-type system derived from the self-regular proximity. The iterate is then updated, with the iterates staying in a certain neighborhood of the central path until an approximate solution to the problem is found. By extensively exploring some intriguing properties of self-regular functions, the authors establish that the complexity of large-update IPMs can come arbitrarily close to the best known iteration bounds of IPMs. Researchers and postgraduate students in all areas of linear and nonlinear optimization will find this book an important and invaluable aid to their work.

Interior-point methods. --- Mathematical optimization. --- Programming (Mathematics). --- Mathematical optimization --- Interior-point methods --- Programming (Mathematics) --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Mathematical programming --- Goal programming --- Algorithms --- Functional equations --- Operations research --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Simulation methods --- System analysis --- 519.85 --- 681.3*G16 --- 681.3*G16 Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- 519.85 Mathematical programming --- Accuracy and precision. --- Algorithm. --- Analysis of algorithms. --- Analytic function. --- Associative property. --- Barrier function. --- Binary number. --- Block matrix. --- Combination. --- Combinatorial optimization. --- Combinatorics. --- Complexity. --- Conic optimization. --- Continuous optimization. --- Control theory. --- Convex optimization. --- Delft University of Technology. --- Derivative. --- Differentiable function. --- Directional derivative. --- Division by zero. --- Dual space. --- Duality (mathematics). --- Duality gap. --- Eigenvalues and eigenvectors. --- Embedding. --- Equation. --- Estimation. --- Existential quantification. --- Explanation. --- Feasible region. --- Filter design. --- Function (mathematics). --- Implementation. --- Instance (computer science). --- Invertible matrix. --- Iteration. --- Jacobian matrix and determinant. --- Jordan algebra. --- Karmarkar's algorithm. --- Karush–Kuhn–Tucker conditions. --- Line search. --- Linear complementarity problem. --- Linear function. --- Linear programming. --- Lipschitz continuity. --- Local convergence. --- Loss function. --- Mathematician. --- Mathematics. --- Matrix function. --- McMaster University. --- Monograph. --- Multiplication operator. --- Newton's method. --- Nonlinear programming. --- Nonlinear system. --- Notation. --- Operations research. --- Optimal control. --- Optimization problem. --- Parameter (computer programming). --- Parameter. --- Pattern recognition. --- Polyhedron. --- Polynomial. --- Positive semidefinite. --- Positive-definite matrix. --- Quadratic function. --- Requirement. --- Result. --- Scientific notation. --- Second derivative. --- Self-concordant function. --- Sensitivity analysis. --- Sign (mathematics). --- Signal processing. --- Simplex algorithm. --- Simultaneous equations. --- Singular value. --- Smoothness. --- Solution set. --- Solver. --- Special case. --- Subset. --- Suggestion. --- Technical report. --- Theorem. --- Theory. --- Time complexity. --- Two-dimensional space. --- Upper and lower bounds. --- Variable (computer science). --- Variable (mathematics). --- Variational inequality. --- Variational principle. --- Without loss of generality. --- Worst-case complexity. --- Yurii Nesterov. --- Mathematical Optimization --- Mathematics --- Programming (mathematics)