Continuous Optimization For Data Science
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A01=Moshe Haviv
Allocation Problems
Author_Moshe Haviv
Barrier Functions
Best Linear Unbiased Estimator
Category=PBKQ
Category=PBU
Concave Functions
Constrained Optimization
Convex Functions
Convex Programming
Convex Sets
Cubic Fitting
Curve Fitting
Dual Variables
Duality
eq_isMigrated=1
eq_nobargain
Expectation-maximization Algorithm
False Position Method
Feasible Solution
Fibonacci Search
First Order Condition
Gradient Descent
Karush-Kuhn-tacker
KKT
Lagrange Multipliers
Lagrangian Method
Least Squares
Line Search
Linear Constrains
Linear Neural Networks
Linear Programming
Linear Regression
Local Maximum
Local Minimum
Logistic Regression
Maximum Likelihood Estimators
MLE
Newton's Method
Non-linear Programming
Penalty Functions
Quadratic Fitting
Quadratic Programming
Quasi-Newton
Rate of Convergence
Representation Theorem
Second Order Condition
Sensitivity Analysis
Shadow Price
Simplex
Steepest Descent
Support Vector Machine
Transportation Problem
Unconstrained Optimization
Unimodal Function
Product details
- ISBN 9789811299193
- Publication Date: 25 Jul 2025
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
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The text is divided into three main parts: unconstrained optimization, constrained optimization, and linear programming. The first part addresses unconstrained optimization in single-variable and multivariable functions, introducing key algorithms such as steepest descent, Newton, and quasi-Newton methods.The second part focuses on constrained optimization, starting with linear equality constraints and extending to more general cases, including inequality constraints. It details optimality conditions, sensitivity analysis, and relevant algorithms for solving these problems.The third part covers linear programming, presenting the formulation of LP problems, the simplex algorithm, and sensitivity analysis. Throughout, the text provides numerous applications to data science, such as linear regression, maximum likelihood estimation, expectation-maximization algorithms, support vector machines, and linear neural networks.
