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Optimization theory is designed to find the best ways of doing things. The structures of animals, their movements, their behavior, and their life histories have all been shaped by the optimizing processes of evolution or of learning by trial and error. In this revised edition of R. McNeill Alexander's widely acclaimed Optima for Animals, we see how extraordinarily diverse branches of biology are illuminated by the powerful methods of optimization theory. What is the best strength for a bone? Too weak a bone will probably break but an excessively stout one will be cumbersome. At what speed should humans change from walking to running? Should a bird take only big juicy worms or should it eat every worm it finds, and do birds make the best choices? Why do the males of some species of fishes and the females of others look after the young, while the young of others are looked after by both parents or neither? Is it possible that all these policies can be optimal, in different circumstances? This book shows how these and many other questions can be answered. The mathematics involved is explained very simply, with biology students in mind, but the book is not just for them. It is also for professionals, ranging from teachers to researchers.

Assessor strategy. --- Barbary doves. --- Bourgeois strategy. --- Cuckolder strategy. --- Dove strategy. --- Haplochromis. --- Hawk strategy. --- Junco. --- Maxwell convention. --- Onchorhynchus. --- Pararge aegeria. --- Pterophylium. --- Vespinae. --- Viverridae. --- Zyxomma. --- albatrosses. --- angelfish. --- bang-bang control. --- buckling. --- butterflies. --- catastrophe theory. --- clutch size. --- computer simulation. --- dynamic programming. --- efficiency. --- energy intake. --- feeding strategies. --- fitness component. --- games theory. --- gradient. --- growth and reproduction. --- integrals. --- intercept. --- marginal value theorem. --- queens. --- red deer. --- risk of starvation. --- rotifer. --- section modulus. --- semicircular canals. --- state variable. --- territories. --- tortoises. --- walking.

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This self-contained introduction to the distributed control of robotic networks offers a distinctive blend of computer science and control theory. The book presents a broad set of tools for understanding coordination algorithms, determining their correctness, and assessing their complexity; and it analyzes various cooperative strategies for tasks such as consensus, rendezvous, connectivity maintenance, deployment, and boundary estimation. The unifying theme is a formal model for robotic networks that explicitly incorporates their communication, sensing, control, and processing capabilities--a model that in turn leads to a common formal language to describe and analyze coordination algorithms. Written for first- and second-year graduate students in control and robotics, the book will also be useful to researchers in control theory, robotics, distributed algorithms, and automata theory. The book provides explanations of the basic concepts and main results, as well as numerous examples and exercises. Self-contained exposition of graph-theoretic concepts, distributed algorithms, and complexity measures for processor networks with fixed interconnection topology and for robotic networks with position-dependent interconnection topology Detailed treatment of averaging and consensus algorithms interpreted as linear iterations on synchronous networks Introduction of geometric notions such as partitions, proximity graphs, and multicenter functions Detailed treatment of motion coordination algorithms for deployment, rendezvous, connectivity maintenance, and boundary estimation

Robotics. --- Computer algorithms. --- Robots --- Automation --- Machine theory --- Robot control --- Robotics --- Algorithms --- Control systems. --- Computer algorithms --- Control systems --- 1-center problem. --- Adjacency matrix. --- Aggregate function. --- Algebraic connectivity. --- Algebraic topology (object). --- Algorithm. --- Analysis of algorithms. --- Approximation algorithm. --- Asynchronous system. --- Bellman–Ford algorithm. --- Bifurcation theory. --- Bounded set (topological vector space). --- Calculation. --- Cartesian product. --- Centroid. --- Chebyshev center. --- Circulant matrix. --- Circumscribed circle. --- Cluster analysis. --- Combinatorial optimization. --- Combinatorics. --- Communication complexity. --- Computation. --- Computational complexity theory. --- Computational geometry. --- Computational model. --- Computer simulation. --- Computer vision. --- Connected component (graph theory). --- Connectivity (graph theory). --- Consensus (computer science). --- Control function (econometrics). --- Differentiable function. --- Dijkstra's algorithm. --- Dimensional analysis. --- Directed acyclic graph. --- Directed graph. --- Discrete time and continuous time. --- Disk (mathematics). --- Distributed algorithm. --- Doubly stochastic matrix. --- Dynamical system. --- Eigenvalues and eigenvectors. --- Estimation. --- Euclidean space. --- Function composition. --- Hybrid system. --- Information theory. --- Initial condition. --- Instance (computer science). --- Invariance principle (linguistics). --- Invertible matrix. --- Iteration. --- Iterative method. --- Kinematics. --- Laplacian matrix. --- Leader election. --- Linear dynamical system. --- Linear interpolation. --- Linear programming. --- Lipschitz continuity. --- Lyapunov function. --- Markov chain. --- Mathematical induction. --- Mathematical optimization. --- Mobile robot. --- Motion planning. --- Multi-agent system. --- Network model. --- Network topology. --- Norm (mathematics). --- Numerical integration. --- Optimal control. --- Optimization problem. --- Parameter (computer programming). --- Partition of a set. --- Percolation theory. --- Permutation matrix. --- Polytope. --- Proportionality (mathematics). --- Quantifier (logic). --- Quantization (signal processing). --- Robustness (computer science). --- Scientific notation. --- Sensor. --- Set (mathematics). --- Simply connected space. --- Simulation. --- Simultaneous equations. --- State space. --- State variable. --- Stochastic matrix. --- Stochastic. --- Strongly connected component. --- Synchronous network. --- Theorem. --- Time complexity. --- Topology. --- Variable (mathematics). --- Vector field.

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Written by one of the leading experts in the field, this book focuses on the interplay between model specification, data collection, and econometric testing of dynamic asset pricing models. The first several chapters provide an in-depth treatment of the econometric methods used in analyzing financial time-series models. The remainder explores the goodness-of-fit of preference-based and no-arbitrage models of equity returns and the term structure of interest rates; equity and fixed-income derivatives prices; and the prices of defaultable securities. Singleton addresses the restrictions on t

Capital assets pricing model. --- Pricing --- Econometric models. --- Arbitrage. --- Asymptotic distribution. --- Autocorrelation. --- Autocovariance. --- Autoregressive conditional heteroskedasticity. --- Bayesian inference. --- Bayesian probability. --- Bond Yield. --- Capital asset pricing model. --- Central limit theorem. --- Collateral Value. --- Conditional expectation. --- Conditional probability distribution. --- Conditional variance. --- Consistent estimator. --- Correlation and dependence. --- Covariance function. --- Covariance matrix. --- Credit risk. --- Credit spread (options). --- Discount function. --- Discrete time and continuous time. --- Doubly stochastic model. --- Dynamic pricing. --- Econometric model. --- Economic equilibrium. --- Economics. --- Equity premium puzzle. --- Ergodic process. --- Estimation theory. --- Estimation. --- Estimator. --- Expectations hypothesis. --- Expected value. --- Forecasting. --- Forward price. --- Forward rate. --- General equilibrium theory. --- Generalized method of moments. --- High-yield debt. --- Inference. --- Interest rate risk. --- Interest rate. --- Investment Horizon. --- Investment strategy. --- Investor. --- Joint probability distribution. --- LIBOR market model. --- Leverage (finance). --- Likelihood function. --- Liquidity premium. --- Liquidity risk. --- Margin (finance). --- Marginal rate of substitution. --- Marginal utility. --- Market Risk Premium. --- Market capitalization. --- Market liquidity. --- Market portfolio. --- Market price. --- Market value. --- Markov model. --- Markov process. --- Mathematical finance. --- Monetary policy. --- Objective Probability. --- Option (finance). --- Parameter. --- Partial equilibrium. --- Portfolio insurance. --- Precautionary savings. --- Predictability. --- Preference (economics). --- Present value. --- Price index. --- Pricing. --- Principal component analysis. --- Probability. --- Real interest rate. --- Repurchase agreement. --- Revaluation of fixed assets. --- Risk aversion. --- Risk management. --- Risk premium. --- Skewness. --- Special case. --- Standard deviation. --- State variable. --- Statistic. --- Stochastic differential equation. --- Stochastic volatility. --- Supply (economics). --- Time series. --- Underlying Security. --- Utility maximization problem. --- Utility. --- Variable (mathematics). --- Vector autoregression. --- Yield curve. --- Yield spread.

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Every minute, 34 new patients are diagnosed with cancer globally. Although over the past 50 years treatments have improved and survival rates have increased dramatically for several types of cancers, many remain incurable. Several aggressive types of blood and solid cancers form when mutations occur in a critical cellular signaling pathway, the JAK-STAT pathway; (Janus Kinase-Signal Transducer and Activator of Transcription). Currently, there are no clinically available drugs that target the oncogenic STAT3/5 proteins in particular or their Gain of Function hyperactive mutant products. Here, we summarize targeting approaches on STAT3/5, as the field moves towards clinical applications as well as we illuminate on upstream or downstream JAK-STAT pathway interference with kinase inhibitors, heat shock protein blockers or changing nuclear import/export processes. We cover the design paradigms and medicinal chemistry approaches to illuminate progress and challenges in understanding the pleiotropic role of STAT3 and STAT5 in oncogenesis, the microenvironment, the immune system in particular, all culminating in a complex interplay towards cancer progression.

Research & information: general --- Biology, life sciences --- multiple myeloma --- STAT3 --- S3I-1757 --- nanoparticle --- CD38 --- siRNA/RNAi --- polyethylenimine --- PEI --- lipopolyplex --- siRNA delivery --- glioma --- glioblastoma --- STAT5 --- AKT --- ERK1/2 --- prolactin --- androgens --- prostate cancer --- knockout --- escape mechanisms --- stem/progenitor cells --- cell hierarchy --- cancer --- CD4+ T cells --- CD8+ T cells --- myeloid cells --- immune check point --- hepatitis C virus (HCV) --- cirrhosis --- hepatocellular carcinoma (HCC) --- endoplasmic reticulum (ER) stress --- oxidative stress (OS) --- unfolded protein response (UPR) --- microRNA-122 (miR-122) --- nuclear factor erythroid 2-related factor 2 (NRF2) --- signal transducer and activator of transcription 3 (STAT3) --- hepatocyte nuclear factor 4 alpha (HNF4A) --- solid cancers --- cell cycle --- apoptosis --- inflammation --- mitochondria --- stemness --- tumor suppression --- melanoma --- autoimmune disease --- immunotherapy --- tumor-immune cell interactions --- breast cancer --- PD-L1 --- M2 macrophages --- NK cells --- STAT3 inhibitor XIII --- hedging --- transaction costs --- dynamic programming --- risk management --- post-decision state variable --- cancer progression --- cancer-stem cell --- cytokine --- therapy resistance --- metastasis --- immunosuppression --- tumor microenvironment --- proliferation --- tyrosine kinase 2 --- JAK family of protein tyrosine kinases --- signal transducer and activator of transcription --- cytokine receptor signaling --- gain-of-function mutation --- tumorigenesis --- ADAM17 --- interleukin-6 --- trans-signaling --- epidermal growth factor receptor (EGF-R) --- shedding --- metalloprotease --- tumor necrosis factor alpha (TNFα) --- inflammation associated cancer --- colon cancer --- lung cancer --- SH2 domain --- mutations --- autosomal-dominant hyper IgE syndrome --- inflammatory hepatocellular adenomas --- T-cell large granular lymphocytic leukemia --- T-cell prolymphocytic leukemia --- growth hormone insensitivity syndrome --- nuclear pore complex --- nuclear transport receptors --- nucleocytoplasmic shuttling --- targeting --- tumor-associated macrophages --- adoptive T cell therapy --- immune suppression --- STAT transcription factors --- JAK --- STAT --- T-PLL --- T-cell leukemia --- meta-analysis --- STAT5B signaling --- small-molecule inhibitors --- cancer models --- companion animals --- comparative oncology --- pharmacological inhibitor --- STAT5 signaling --- chemotherapy resistance --- myeloid leukemia --- heat shock proteins --- chaperones --- stabilization --- targeted therapy --- ovarian cancer --- hematopoietic cancers --- therapeutic targeting --- pharmacological inhibitors --- mTOR --- Bone Marrow Failure Syndromes --- lymphocytes --- lymphoma --- T-cells --- RHOA --- NGS --- MPN --- JAK2 V617F --- neoplastic stem cells --- multiple myeloma --- STAT3 --- S3I-1757 --- nanoparticle --- CD38 --- siRNA/RNAi --- polyethylenimine --- PEI --- lipopolyplex --- siRNA delivery --- glioma --- glioblastoma --- STAT5 --- AKT --- ERK1/2 --- prolactin --- androgens --- prostate cancer --- knockout --- escape mechanisms --- stem/progenitor cells --- cell hierarchy --- cancer --- CD4+ T cells --- CD8+ T cells --- myeloid cells --- immune check point --- hepatitis C virus (HCV) --- cirrhosis --- hepatocellular carcinoma (HCC) --- endoplasmic reticulum (ER) stress --- oxidative stress (OS) --- unfolded protein response (UPR) --- microRNA-122 (miR-122) --- nuclear factor erythroid 2-related factor 2 (NRF2) --- signal transducer and activator of transcription 3 (STAT3) --- hepatocyte nuclear factor 4 alpha (HNF4A) --- solid cancers --- cell cycle --- apoptosis --- inflammation --- mitochondria --- stemness --- tumor suppression --- melanoma --- autoimmune disease --- immunotherapy --- tumor-immune cell interactions --- breast cancer --- PD-L1 --- M2 macrophages --- NK cells --- STAT3 inhibitor XIII --- hedging --- transaction costs --- dynamic programming --- risk management --- post-decision state variable --- cancer progression --- cancer-stem cell --- cytokine --- therapy resistance --- metastasis --- immunosuppression --- tumor microenvironment --- proliferation --- tyrosine kinase 2 --- JAK family of protein tyrosine kinases --- signal transducer and activator of transcription --- cytokine receptor signaling --- gain-of-function mutation --- tumorigenesis --- ADAM17 --- interleukin-6 --- trans-signaling --- epidermal growth factor receptor (EGF-R) --- shedding --- metalloprotease --- tumor necrosis factor alpha (TNFα) --- inflammation associated cancer --- colon cancer --- lung cancer --- SH2 domain --- mutations --- autosomal-dominant hyper IgE syndrome --- inflammatory hepatocellular adenomas --- T-cell large granular lymphocytic leukemia --- T-cell prolymphocytic leukemia --- growth hormone insensitivity syndrome --- nuclear pore complex --- nuclear transport receptors --- nucleocytoplasmic shuttling --- targeting --- tumor-associated macrophages --- adoptive T cell therapy --- immune suppression --- STAT transcription factors --- JAK --- STAT --- T-PLL --- T-cell leukemia --- meta-analysis --- STAT5B signaling --- small-molecule inhibitors --- cancer models --- companion animals --- comparative oncology --- pharmacological inhibitor --- STAT5 signaling --- chemotherapy resistance --- myeloid leukemia --- heat shock proteins --- chaperones --- stabilization --- targeted therapy --- ovarian cancer --- hematopoietic cancers --- therapeutic targeting --- pharmacological inhibitors --- mTOR --- Bone Marrow Failure Syndromes --- lymphocytes --- lymphoma --- T-cells --- RHOA --- NGS --- MPN --- JAK2 V617F --- neoplastic stem cells

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Robust optimization is a fairly new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. The authors are the principal developers of robust optimization.

Robust optimization. --- Linear programming. --- 519.8 --- 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) --- 519.8 Operational research --- Operational research --- Robust optimization --- Linear programming --- Optimisation robuste --- Programmation linéaire --- Optimization, Robust --- RO (Robust optimization) --- Mathematical optimization --- Production scheduling --- Programming (Mathematics) --- 0O. --- Accuracy and precision. --- Additive model. --- Almost surely. --- Approximation algorithm. --- Approximation. --- Best, worst and average case. --- Bifurcation theory. --- Big O notation. --- Candidate solution. --- Central limit theorem. --- Chaos theory. --- Coefficient. --- Computational complexity theory. --- Constrained optimization. --- Convex hull. --- Convex optimization. --- Convex set. --- Cumulative distribution function. --- Curse of dimensionality. --- Decision problem. --- Decision rule. --- Degeneracy (mathematics). --- Diagram (category theory). --- Duality (optimization). --- Dynamic programming. --- Exponential function. --- Feasible region. --- Floor and ceiling functions. --- For All Practical Purposes. --- Free product. --- Ideal solution. --- Identity matrix. --- Inequality (mathematics). --- Infimum and supremum. --- Integer programming. --- Law of large numbers. --- Likelihood-ratio test. --- Linear dynamical system. --- Linear inequality. --- Linear map. --- Linear matrix inequality. --- Linear regression. --- Loss function. --- Margin classifier. --- Markov chain. --- Markov decision process. --- Mathematical optimization. --- Max-plus algebra. --- Maxima and minima. --- Multivariate normal distribution. --- NP-hardness. --- Norm (mathematics). --- Normal distribution. --- Optimal control. --- Optimization problem. --- Orientability. --- P versus NP problem. --- Pairwise. --- Parameter. --- Parametric family. --- Probability distribution. --- Probability. --- Proportionality (mathematics). --- Quantity. --- Random variable. --- Relative interior. --- Robust control. --- Robust decision-making. --- Semi-infinite. --- Sensitivity analysis. --- Simple set. --- Singular value. --- Skew-symmetric matrix. --- Slack variable. --- Special case. --- Spherical model. --- Spline (mathematics). --- State variable. --- Stochastic calculus. --- Stochastic control. --- Stochastic optimization. --- Stochastic programming. --- Stochastic. --- Strong duality. --- Support vector machine. --- Theorem. --- Time complexity. --- Uncertainty. --- Uniform distribution (discrete). --- Unimodality. --- Upper and lower bounds. --- Variable (mathematics). --- Virtual displacement. --- Weak duality. --- Wiener filter. --- With high probability. --- Without loss of generality.