dgfoki.blogg.se

Cogs ucsd courses
Cogs ucsd courses





cogs ucsd courses

Prerequisites: Linear Algebra is recommended. The objective of the course is to provide students the background and techniques for scientific computing and system optimization. The topics include convex sets, functions, optimality conditions, duality concepts, gradient descent, conjugate gradient, interior-point methods, and applications. We study the formulations and algorithms solving convex optimization problems. Convex Optimization Formulations and Algorithms (4) Topics include approximation, randomized algorithms, probabilistic analysis, heuristics, online algorithms, competitive analysis, models of memory hierarchy, parallel algorithms, number-theoretic algorithms, cryptanalysis, computational geometry, computational biology, network algorithms, VLSI CAD algorithms. Modern advances in design and analysis of algorithms.

cogs ucsd courses

Divide-and-conquer, dynamic programming, data structures, graph search, algebraic problems, randomized algorithms, lower bounds, probabilistic analysis, parallel algorithms. The basic techniques for the design and analysis of algorithms. Prerequisites: graduate standing.ĬSE 202. Recommended preparation: CSE 200 is recommended. Polynomial-time hierarchy (PH) BPP in second level of PH Savitch's theorem NL=coNL non-uniform and circuit complexity some circuit lower bounds IP=PSPACE probabilistic proof checking (PCP) Application of PCP to approximation hardness Complexity of proof systems Parallel complexity classes NC and AC P-completeness. sets, many-one reductions TIME(t(n)), SPACE(s(n)) and general relations between these classes L, P, PSPACE, NP NP-completeness hierarchy theorems RP, BPP. Computability and Complexity (4)Ĭomputability review, including halting problem, decidable sets, r.e.







Cogs ucsd courses