In this paper we bound the infimum of the ratio of adaptive to nonadaptive information for linear problems in Banach spaces. This result resolves the conjecture on adaption, showing that adaption can help for linear problems. Letting (Y denote the above infimum, and or the same infimum over all linear problems with Hilbert space range, we show that ) 5 a 5 V%%% and V’?/2 I a2 5 e. Analogous res...

Continuous optimization problems are frequently encountered in the fields of mathematics and engineering. Based on their properties, continuous optimization problems can be briefly classified into four categories: unconstrained singleobjective optimization problems (USOPs), constrained single-objective optimization problems (CSOPs), unconstrained multi-objective optimization problems (UMOPs), a...

Existence of the smallest, greatest, minimal, maximal and unique continuous solutions to distributional Cauchy problems, as well as their dependence on the data, are studied. The main tools are a continuous primitive integral and fixed point results in function spaces.

This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females...

We prove that the notion of KT-invexity for continuous-time nonlinear optimization problem is a necessary and sufficient condition for global optimality of a KarushKuhn-Tucker point.

In this work, we study continuous reformulations of zero–one programming problems. We prove that, under suitable conditions, the optimal solutions of a zero–one programming problem can be obtained by solving a specific continuous problem.

in this paper, a new continuous ant colony optimization (caco) algorithm is proposed for optimal reservoir operation. the paper presents a new method of determining and setting a complete set of control parameters for any given problem, saving the user from a tedious trial and error based approach to determine them. the paper also proposes an elitist strategy for caco algorithm where best solut...

This paper presents and discusses examples of mechanical i n fe rence problems which must be so lved in order to cons t ruc t e f f e c t i v e mechanical speech understanding systems. The examples are taken from incrementa l s imu la t i ons of a p ro to t ype speech understanding system which w i l l use s y n t a c t i c , semant ic , and pragmat ic i n f o r m a t i o n as w e l l as a c o ...

Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called ‘‘var...

We study the budgeted bandit problem, where each arm is associated with both a reward and a cost. In a budgeted bandit problem, the objective is to design an arm pulling algorithm in order to maximize the total reward before the budget runs out. In this work, we study both multi-armed bandits and linear bandits, and focus on the setting with continuous random costs. We propose an upper confiden...