Effective constrained optimization algorithms have been proposed for engineering problems recently. It is common to consider constraint violation and algorithm as two separate parts. In this study, a pbest selection mechanism integrate the current mutation strategy in problems. Based on improved method, an adaptive differential evolution approach proposed, which helps population jump out of inf...

We consider global convergence properties of the augmented Lagrangian methods on problems with degenerate constraints, with a special emphasis on mathematical programs with complementarity constraints (MPCC). In the general case, we show convergence to stationary points of the problem under an error bound condition for the feasible set (which is weaker than constraint qualifications), assuming ...

An instance of the Constraint Satisfaction Problem (CSP) is given by a family of constraints on overlapping sets of variables, and the goal is to assign values from a fixed domain to the variables so that all constraints are satisfied. In the optimization version, the goal is to maximize the number of satisfied constraints. An approximation algorithm for CSP is called robust if it outputs an as...

The paper focuses on the convergence rate of the augmented Lagrangian method for nonlinear second-order cone optimization problems. Under a set of assumptions of sufficient conditions, including the componentwise strict complementarity condition, the constraint nondegeneracy condition and the second order sufficient condition, we first study some properties of the augmented Lagrangian and then ...

The use of semileptonic decays has become standard in constraining the unitarity triangle. Since precise calculations of these are very challenging, we propose an entirely new approach. The ε{K} constraint, which depends extremely sensitively on |V{cb}|, is replaced by the interplay between ε{K}, BR(B→τν), and ΔM{B{s}}. Improvements on the B→τν branching ratio and on the lattice determinations ...

When designing controllers with robust performance and stabilization requirements, H-infinity synthesis is a common tool to use. These controllers are often obtained by solving mathematical optimization problems. The controllers that result from these algorithms are typically of very high order, which complicates implementation. Low order controllers are usually desired, since they are consider...

We give a nonstandard variant of Jordan’s proof of the Jordan curve theorem which is free of the defects his contemporaries criticized and avoids the epsilontic burden of the classical proof. The proof is selfcontained, except for the Jordan theorem for polygons taken for granted.

We study the problem of maximizing a monotone submodular function subject to Multiple Knapsack constraint. The input is set I items, each has non-negative weight, and bins arbitrary capacities. Also, we are given submodular, f over subsets items. objective find packing subset items A⊆I in such that f(A) maximized. Our main result an almost optimal polynomial time (1−e−1−ε)-approximation algorit...

Multi-objective reinforcement learning involves the use of techniques to address problems with multiple objectives. To resolve this, we a hybrid multi-objective optimization method that provides mathematical guarantee all policies belonging Pareto Front can be found. The hybridization gave rise Q-Managed, which is given by ε−constraint and Q-Learning algorithm, where first limits environment dy...