scirs2_optimize/neuromorphic/

memristive_optimization.rs

//! Memristive Optimization
//!
//! This module implements optimization algorithms inspired by memristors -
//! resistive devices whose resistance depends on the history of applied voltage/current.
//! Features advanced memristor models, crossbar architectures, and variability modeling.

use crate::error::OptimizeResult;
use crate::result::OptimizeResults;
use scirs2_core::error::CoreResult as Result;
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::random::Rng;
use scirs2_core::simd_ops::SimdUnifiedOps;

/// Advanced memristor models
#[derive(Debug, Clone, Copy)]
pub enum MemristorModel {
    /// Linear ionic drift model
    LinearIonicDrift,
    /// Nonlinear ionic drift with window functions
    NonlinearIonicDrift,
    /// Simmons tunnel barrier model
    SimmonsTunnelBarrier,
    /// Team model with exponential switching
    TeamModel,
    /// Biolek model with threshold switching
    BiolekModel,
}

/// Memristor device parameters
#[derive(Debug, Clone)]
pub struct MemristorParameters {
    /// Physical length of device
    pub length: f64,
    /// Mobility of dopants
    pub mobility: f64,
    /// Ron resistance (fully doped)
    pub r_on: f64,
    /// Roff resistance (undoped)
    pub r_off: f64,
    /// Initial doped region width
    pub initial_x: f64,
    /// Temperature coefficient
    pub temp_coeff: f64,
    /// Device variability (standard deviation)
    pub variability: f64,
    /// Nonlinearity parameters
    pub p_coeff: f64,
    pub q_coeff: f64,
}

impl Default for MemristorParameters {
    fn default() -> Self {
        Self {
            length: 10e-9,     // 10 nm
            mobility: 1e-10,   // m²/s/V
            r_on: 100.0,       // Ohms
            r_off: 16000.0,    // Ohms
            initial_x: 0.5,    // Normalized position
            temp_coeff: 0.001, // 1/K
            variability: 0.05, // 5% standard deviation
            p_coeff: 10.0,     // Nonlinearity parameter
            q_coeff: 10.0,     // Nonlinearity parameter
        }
    }
}

/// Advanced memristor device model
#[derive(Debug, Clone)]
pub struct Memristor {
    /// Current resistance state
    pub resistance: f64,
    /// State variable (normalized position of doped region boundary)
    pub state: f64,
    /// Device parameters
    pub params: MemristorParameters,
    /// Model type
    pub model: MemristorModel,
    /// Current temperature
    pub temperature: f64,
    /// Device-specific variability factor
    pub variability_factor: f64,
    /// History of applied voltages (for hysteresis modeling)
    pub voltage_history: Vec<f64>,
    /// Maximum history length
    pub max_history: usize,
}

impl Memristor {
    /// Create new memristor with advanced model
    pub fn new(params: MemristorParameters, model: MemristorModel) -> Self {
        let initial_resistance =
            params.r_on + (params.r_off - params.r_on) * (1.0 - params.initial_x);
        let variability_factor = if params.variability > 0.0 {
            1.0 + (scirs2_core::random::rng().random::<f64>() - 0.5) * 2.0 * params.variability
        } else {
            1.0
        };

        Self {
            resistance: initial_resistance * variability_factor,
            state: params.initial_x,
            params,
            model,
            temperature: 300.0, // Room temperature in Kelvin
            variability_factor,
            voltage_history: Vec::new(),
            max_history: 10,
        }
    }

    /// Update memristor state using advanced physics models
    pub fn update(&mut self, voltage: f64, dt: f64) {
        // Store voltage history for hysteresis modeling
        self.voltage_history.push(voltage);
        if self.voltage_history.len() > self.max_history {
            self.voltage_history.remove(0);
        }

        // Temperature-dependent mobility
        let temp_factor = 1.0 + self.params.temp_coeff * (self.temperature - 300.0);
        let effective_mobility = self.params.mobility * temp_factor;

        match self.model {
            MemristorModel::LinearIonicDrift => {
                self.update_linear_drift(voltage, dt, effective_mobility);
            }
            MemristorModel::NonlinearIonicDrift => {
                self.update_nonlinear_drift(voltage, dt, effective_mobility);
            }
            MemristorModel::SimmonsTunnelBarrier => {
                self.update_simmons_model(voltage, dt);
            }
            MemristorModel::TeamModel => {
                self.update_team_model(voltage, dt);
            }
            MemristorModel::BiolekModel => {
                self.update_biolek_model(voltage, dt);
            }
        }

        // Update resistance based on new state
        self.update_resistance();
    }

    /// Linear ionic drift model
    fn update_linear_drift(&mut self, voltage: f64, dt: f64, mobility: f64) {
        let dx_dt = (mobility * self.params.r_on) / self.params.length.powi(2) * voltage;
        self.state += dx_dt * dt;
        self.state = self.state.max(0.0).min(1.0);
    }

    /// Nonlinear ionic drift with window functions
    fn update_nonlinear_drift(&mut self, voltage: f64, dt: f64, mobility: f64) {
        // Joglekar window function
        let window = if voltage > 0.0 {
            self.state * (1.0 - self.state).powf(self.params.p_coeff)
        } else {
            self.state.powf(self.params.p_coeff) * (1.0 - self.state)
        };

        let dx_dt = (mobility * self.params.r_on) / self.params.length.powi(2) * voltage * window;
        self.state += dx_dt * dt;
        self.state = self.state.max(0.0).min(1.0);
    }

    /// Simmons tunnel barrier model
    fn update_simmons_model(&mut self, voltage: f64, dt: f64) {
        let _beta = 0.8; // Barrier modification parameter
        let v_th = 0.16; // Threshold voltage

        if voltage.abs() > v_th {
            let sign = voltage.signum();
            let exp_term = (-(voltage.abs() - v_th) / 0.3).exp();
            let dx_dt = sign * 10e-15 * (1.0 - exp_term);

            self.state += dx_dt * dt / self.params.length;
            self.state = self.state.max(0.0).min(1.0);
        }
    }

    /// TEAM model with exponential switching
    fn update_team_model(&mut self, voltage: f64, dt: f64) {
        let v_on = 0.3; // Threshold for SET
        let v_off = -0.5; // Threshold for RESET
        let k_on = 8e-13; // Rate constant for SET
        let k_off = 8e-13; // Rate constant for RESET

        if voltage > v_on {
            let dx_dt = k_on * ((voltage / v_on) - 1.0).exp();
            self.state += dx_dt * dt;
        } else if voltage < v_off {
            let dx_dt = -k_off * ((voltage.abs() / v_off.abs()) - 1.0).exp();
            self.state += dx_dt * dt;
        }

        self.state = self.state.max(0.0).min(1.0);
    }

    /// Biolek model with threshold and polarity
    fn update_biolek_model(&mut self, voltage: f64, dt: f64) {
        let v_th = 1.0; // Threshold voltage

        if voltage.abs() > v_th {
            let window = if voltage > 0.0 {
                1.0 - (2.0 * self.state - 1.0).powi(2 * self.params.p_coeff as i32)
            } else {
                1.0 - (2.0 * self.state - 1.0).powi(2 * self.params.q_coeff as i32)
            };

            let dx_dt = voltage * window * 1e-12;
            self.state += dx_dt * dt;
            self.state = self.state.max(0.0).min(1.0);
        }
    }

    /// Update resistance based on current state
    fn update_resistance(&mut self) {
        // Account for device variability
        let base_resistance =
            self.params.r_on * self.state + self.params.r_off * (1.0 - self.state);
        self.resistance = base_resistance * self.variability_factor;
    }

    /// Get conductance (inverse of resistance)
    pub fn conductance(&self) -> f64 {
        1.0 / self.resistance
    }

    /// Set device temperature
    pub fn set_temperature(&mut self, temperature: f64) {
        self.temperature = temperature;
    }

    /// Get current power dissipation for given voltage
    pub fn power_dissipation(&self, voltage: f64) -> f64 {
        voltage.powi(2) / self.resistance
    }

    /// Reset device to initial state
    pub fn reset(&mut self) {
        self.state = self.params.initial_x;
        self.voltage_history.clear();
        self.update_resistance();
    }
}

/// Advanced memristive crossbar architecture
#[derive(Debug, Clone)]
pub struct MemristiveCrossbar {
    /// Array of memristors
    pub memristors: Vec<Vec<Memristor>>,
    /// Dimensions
    pub rows: usize,
    pub cols: usize,
    /// Parasitic resistances (wire resistance)
    pub row_resistance: Array1<f64>,
    pub col_resistance: Array1<f64>,
    /// Voltage compliance limits
    pub v_max: f64,
    pub v_min: f64,
    /// Stuck-at-fault map (true = faulty device)
    pub fault_map: Array2<bool>,
    /// Sneak path compensation
    pub use_sneak_compensation: bool,
    /// Crossbar statistics
    pub stats: CrossbarStats,
}

/// Statistics for crossbar operation
#[derive(Debug, Clone)]
pub struct CrossbarStats {
    /// Total operations performed
    pub operations: usize,
    /// Total power consumption
    pub power_consumption: f64,
    /// Average read time
    pub avg_read_time_ns: f64,
    /// Average write time
    pub avg_write_time_ns: f64,
    /// Number of faulty devices
    pub faulty_devices: usize,
}

impl Default for CrossbarStats {
    fn default() -> Self {
        Self {
            operations: 0,
            power_consumption: 0.0,
            avg_read_time_ns: 1.0,
            avg_write_time_ns: 10.0,
            faulty_devices: 0,
        }
    }
}

impl MemristiveCrossbar {
    /// Create new advanced crossbar
    pub fn new(
        rows: usize,
        cols: usize,
        params: MemristorParameters,
        model: MemristorModel,
    ) -> Self {
        let mut memristors = Vec::with_capacity(rows);
        let mut fault_map = Array2::from_elem((rows, cols), false);
        let mut faulty_count = 0;

        for i in 0..rows {
            let mut row = Vec::with_capacity(cols);
            for j in 0..cols {
                let mut memristor = Memristor::new(params.clone(), model);

                // Introduce random stuck-at faults (1% probability)
                if scirs2_core::random::rng().random::<f64>() < 0.01 {
                    fault_map[[i, j]] = true;
                    faulty_count += 1;
                    // Set to extreme resistance values for stuck faults
                    if scirs2_core::random::rng().random::<bool>() {
                        memristor.resistance = params.r_off * 10.0; // Stuck high
                    } else {
                        memristor.resistance = params.r_on * 0.1; // Stuck low
                    }
                }

                row.push(memristor);
            }
            memristors.push(row);
        }

        // Wire resistance (increases with array size)
        let wire_r_per_cell = 1.0; // Ohms per cell
        let row_resistance = Array1::from_shape_fn(rows, |i| wire_r_per_cell * (i + 1) as f64);
        let col_resistance = Array1::from_shape_fn(cols, |j| wire_r_per_cell * (j + 1) as f64);

        let mut stats = CrossbarStats::default();
        stats.faulty_devices = faulty_count;

        Self {
            memristors,
            rows,
            cols,
            row_resistance,
            col_resistance,
            v_max: 1.5,  // Maximum compliance voltage
            v_min: -1.5, // Minimum compliance voltage
            fault_map,
            use_sneak_compensation: true,
            stats,
        }
    }

    /// Matrix-vector multiplication with non-idealities
    pub fn multiply(&mut self, input: &ArrayView1<f64>) -> Array1<f64> {
        let start_time = std::time::Instant::now();
        let mut output = Array1::zeros(self.rows);

        // SIMD-optimized computation where possible
        if input.len() >= 4 && self.rows >= 4 {
            self.multiply_simd(input, &mut output);
        } else {
            self.multiply_scalar(input, &mut output);
        }

        // Account for parasitic resistances and sneak paths
        if self.use_sneak_compensation {
            self.compensate_sneak_paths(&mut output, input);
        }

        // Update statistics
        self.stats.operations += 1;
        self.stats.power_consumption += self.calculate_read_power(input);
        let elapsed = start_time.elapsed().as_nanos() as f64;
        self.stats.avg_read_time_ns =
            (self.stats.avg_read_time_ns * (self.stats.operations - 1) as f64 + elapsed)
                / self.stats.operations as f64;

        output
    }

    /// SIMD-optimized matrix multiplication
    fn multiply_simd(&self, input: &ArrayView1<f64>, output: &mut Array1<f64>) {
        for i in 0..self.rows {
            let mut sum = 0.0;
            let conductances: Vec<f64> = (0..self.cols)
                .map(|j| {
                    if self.fault_map[[i, j]] {
                        0.0
                    } else {
                        self.memristors[i][j].conductance()
                    }
                })
                .collect();

            // Use SIMD operations for dot product
            if conductances.len() >= input.len() {
                let g_slice = &conductances[..input.len()];
                let g_array = Array1::from(g_slice.to_vec());
                sum = SimdUnifiedOps::simd_dot(&g_array.view(), input);
            }

            output[i] = sum;
        }
    }

    /// Scalar matrix multiplication fallback
    fn multiply_scalar(&self, input: &ArrayView1<f64>, output: &mut Array1<f64>) {
        for i in 0..self.rows {
            for j in 0..self.cols.min(input.len()) {
                if !self.fault_map[[i, j]] {
                    let conductance = self.memristors[i][j].conductance();
                    output[i] += input[j] * conductance;
                }
            }
        }
    }

    /// Compensate for sneak path currents
    fn compensate_sneak_paths(&self, output: &mut Array1<f64>, input: &ArrayView1<f64>) {
        // Simplified sneak path compensation
        // In practice, this would involve solving Kirchhoff's laws
        let _avg_conductance = self.calculate_average_conductance();
        let sneak_compensation_factor = 0.95; // Empirical factor

        for i in 0..output.len() {
            output[i] *= sneak_compensation_factor;
        }
    }

    /// Calculate average conductance for sneak path estimation
    fn calculate_average_conductance(&self) -> f64 {
        let mut sum = 0.0;
        let mut count = 0;

        for i in 0..self.rows {
            for j in 0..self.cols {
                if !self.fault_map[[i, j]] {
                    sum += self.memristors[i][j].conductance();
                    count += 1;
                }
            }
        }

        if count > 0 {
            sum / count as f64
        } else {
            0.0
        }
    }

    /// Calculate power consumption for read operation
    fn calculate_read_power(&self, input: &ArrayView1<f64>) -> f64 {
        let mut power = 0.0;
        let read_voltage = 0.1; // Low read voltage

        for i in 0..self.rows {
            for j in 0..self.cols.min(input.len()) {
                if !self.fault_map[[i, j]] && input[j].abs() > 1e-10 {
                    power += self.memristors[i][j].power_dissipation(read_voltage * input[j]);
                }
            }
        }

        power
    }

    /// Advanced update with programming algorithms
    pub fn update(
        &mut self,
        input: &ArrayView1<f64>,
        target: &ArrayView1<f64>,
        learning_rate: f64,
    ) -> Result<()> {
        let start_time = std::time::Instant::now();

        // Compute current output
        let current_output = self.multiply(input);
        let error = target - &current_output;

        // Apply different programming schemes
        for i in 0..self.rows.min(error.len()) {
            for j in 0..self.cols.min(input.len()) {
                if !self.fault_map[[i, j]] {
                    // Compute desired conductance change
                    let desired_delta_g = learning_rate * error[i] * input[j];

                    // Convert to voltage pulses
                    let programming_voltage =
                        self.conductance_change_to_voltage(desired_delta_g, i, j);

                    // Apply voltage compliance
                    let limited_voltage = programming_voltage.max(self.v_min).min(self.v_max);

                    // Update memristor
                    let dt = 1e-6; // 1 microsecond programming pulse
                    self.memristors[i][j].update(limited_voltage, dt);
                }
            }
        }

        // Update statistics
        let elapsed = start_time.elapsed().as_nanos() as f64;
        self.stats.avg_write_time_ns =
            (self.stats.avg_write_time_ns * self.stats.operations as f64 + elapsed)
                / (self.stats.operations + 1) as f64;

        Ok(())
    }

    /// Convert desired conductance change to programming voltage
    fn conductance_change_to_voltage(&self, delta_g: f64, row: usize, col: usize) -> f64 {
        // Simplified model: voltage proportional to desired conductance change
        let current_g = self.memristors[row][col].conductance();
        let relative_change = delta_g / (current_g + 1e-12);

        // Empirical voltage-conductance relationship
        if relative_change > 0.0 {
            0.5 * relative_change.ln().max(-3.0) // SET operation
        } else {
            -0.5 * (-relative_change).ln().max(-3.0) // RESET operation
        }
    }

    /// Perform crossbar refresh to combat drift
    pub fn refresh(&mut self) -> Result<()> {
        for i in 0..self.rows {
            for j in 0..self.cols {
                if !self.fault_map[[i, j]] {
                    // Read current conductance
                    let _target_conductance = self.memristors[i][j].conductance();

                    // Apply refresh pulse to maintain conductance
                    let refresh_voltage = 0.1; // Small refresh voltage
                    self.memristors[i][j].update(refresh_voltage, 1e-7);
                }
            }
        }
        Ok(())
    }

    /// Get crossbar statistics
    pub fn get_stats(&self) -> &CrossbarStats {
        &self.stats
    }

    /// Reset all memristors
    pub fn reset(&mut self) {
        for i in 0..self.rows {
            for j in 0..self.cols {
                if !self.fault_map[[i, j]] {
                    self.memristors[i][j].reset();
                }
            }
        }
        self.stats = CrossbarStats::default();
        self.stats.faulty_devices = self.fault_map.iter().filter(|&&x| x).count();
    }

    /// Get conductance matrix
    pub fn get_conductance_matrix(&self) -> Array2<f64> {
        let mut conductances = Array2::zeros((self.rows, self.cols));
        for i in 0..self.rows {
            for j in 0..self.cols {
                conductances[[i, j]] = if self.fault_map[[i, j]] {
                    0.0
                } else {
                    self.memristors[i][j].conductance()
                };
            }
        }
        conductances
    }
}

/// Advanced memristive optimization algorithms
/// Memristive Gradient Descent Optimizer
#[derive(Debug, Clone)]
pub struct MemristiveOptimizer {
    /// Memristive crossbar for weight storage and computation
    pub crossbar: MemristiveCrossbar,
    /// Current parameter estimates
    pub parameters: Array1<f64>,
    /// Best parameters found
    pub best_parameters: Array1<f64>,
    /// Best objective value
    pub best_objective: f64,
    /// Learning rate
    pub learning_rate: f64,
    /// Momentum coefficient
    pub momentum: f64,
    /// Momentum buffer
    pub momentum_buffer: Array1<f64>,
    /// Iteration counter
    pub nit: usize,
}

impl MemristiveOptimizer {
    /// Create new memristive optimizer
    pub fn new(
        initial_params: Array1<f64>,
        learning_rate: f64,
        momentum: f64,
        memristor_params: MemristorParameters,
        model: MemristorModel,
    ) -> Self {
        let n = initial_params.len();
        let crossbar_size = (n as f64).sqrt().ceil() as usize;
        let crossbar =
            MemristiveCrossbar::new(crossbar_size, crossbar_size, memristor_params, model);

        Self {
            crossbar,
            parameters: initial_params.clone(),
            best_parameters: initial_params.clone(),
            best_objective: f64::INFINITY,
            learning_rate,
            momentum,
            momentum_buffer: Array1::zeros(n),
            nit: 0,
        }
    }

    /// Optimize using memristive crossbar
    pub fn optimize<F>(
        &mut self,
        objective: F,
        max_nit: usize,
    ) -> OptimizeResult<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let mut convergence_history = Vec::new();

        for iter in 0..max_nit {
            // Evaluate current objective
            let current_obj = objective(&self.parameters.view());
            convergence_history.push(current_obj);

            // Update best solution
            if current_obj < self.best_objective {
                self.best_objective = current_obj;
                self.best_parameters = self.parameters.clone();
            }

            // Compute gradient using finite differences
            let gradient = self.compute_finite_diff_gradient(&objective)?;

            // Encode gradient into crossbar input
            let crossbar_input = self.encode_gradient(&gradient);

            // Compute update using crossbar
            let crossbar_output = self.crossbar.multiply(&crossbar_input.view());

            // Decode update and apply to parameters
            let decoded_update = self.decode_update(&crossbar_output);

            // Apply momentum
            self.apply_momentum_update(&decoded_update)?;

            // Update crossbar weights based on performance
            self.update_crossbar_weights(&gradient, current_obj)?;

            // Check convergence
            if self.check_convergence(&convergence_history) {
                break;
            }

            self.nit += 1;

            // Periodic crossbar refresh to combat drift
            if iter % 100 == 0 {
                self.crossbar.refresh()?;
            }
        }

        Ok(OptimizeResults::<f64> {
            x: self.best_parameters.clone(),
            fun: self.best_objective,
            success: self.best_objective < 1e-6,
            nit: self.nit,
            message: "Memristive optimization completed".to_string(),
            jac: None,
            hess: None,
            constr: None,
            nfev: self.nit,
            njev: 0,
            nhev: 0,
            maxcv: 0,
            status: 0,
        })
    }

    /// Compute finite difference gradient
    fn compute_finite_diff_gradient<F>(&self, objective: &F) -> Result<Array1<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let n = self.parameters.len();
        let mut gradient = Array1::zeros(n);
        let h = 1e-6;
        let f0 = objective(&self.parameters.view());

        for i in 0..n {
            let mut params_plus = self.parameters.clone();
            params_plus[i] += h;
            let f_plus = objective(&params_plus.view());
            gradient[i] = (f_plus - f0) / h;
        }

        Ok(gradient)
    }

    /// Encode gradient for crossbar input
    fn encode_gradient(&self, gradient: &Array1<f64>) -> Array1<f64> {
        let crossbar_size = self.crossbar.cols;
        let mut encoded = Array1::zeros(crossbar_size);

        // Simple encoding: map gradient to crossbar input with normalization
        let max_grad = gradient.mapv(|x| x.abs()).fold(0.0, |a, &b| f64::max(a, b));
        if max_grad > 0.0 {
            for i in 0..crossbar_size.min(gradient.len()) {
                encoded[i] = gradient[i] / max_grad;
            }
        }

        encoded
    }

    /// Decode crossbar output to parameter update
    fn decode_update(&self, crossbar_output: &Array1<f64>) -> Array1<f64> {
        let n = self.parameters.len();
        let mut update = Array1::zeros(n);

        // Simple decoding: map crossbar output back to parameter space
        for i in 0..n.min(crossbar_output.len()) {
            update[i] = crossbar_output[i] * self.learning_rate;
        }

        update
    }

    /// Apply momentum update to parameters
    fn apply_momentum_update(&mut self, update: &Array1<f64>) -> Result<()> {
        // Update momentum buffer
        self.momentum_buffer =
            &(self.momentum * &self.momentum_buffer) + &((1.0 - self.momentum) * update);

        // Apply update to parameters
        self.parameters = &self.parameters - &self.momentum_buffer;

        Ok(())
    }

    /// Update crossbar weights based on optimization performance
    fn update_crossbar_weights(
        &mut self,
        gradient: &Array1<f64>,
        objective_value: f64,
    ) -> Result<()> {
        // Adaptive weight update based on gradient and performance
        let performance_factor = (-objective_value / 10.0).exp(); // Better performance = higher factor

        let encoded_gradient = self.encode_gradient(gradient);
        let target_output = &encoded_gradient * performance_factor;

        self.crossbar
            .update(&encoded_gradient.view(), &target_output.view(), 0.01)?;

        Ok(())
    }

    /// Check convergence based on objective history
    fn check_convergence(&self, history: &[f64]) -> bool {
        if history.len() < 10 {
            return false;
        }

        let recent = &history[history.len() - 5..];
        let variance = recent
            .iter()
            .fold(0.0, |acc, &x| acc + (x - recent[0]).powi(2))
            / recent.len() as f64;

        variance < 1e-12
    }
}

/// Memristive gradient descent with basic crossbar
#[allow(dead_code)]
pub fn memristive_gradient_descent<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    learning_rate: f64,
    max_nit: usize,
) -> Result<Array1<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let params = MemristorParameters::default();
    let model = MemristorModel::NonlinearIonicDrift;

    let mut optimizer = MemristiveOptimizer::new(
        initial_params.to_owned(),
        learning_rate,
        0.9, // momentum
        params,
        model,
    );

    let result = optimizer.optimize(objective, max_nit)?;
    Ok(result.x)
}

/// Advanced memristive optimization with custom configuration
#[allow(dead_code)]
pub fn advanced_memristive_optimization<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    learning_rate: f64,
    max_nit: usize,
    memristor_params: MemristorParameters,
    model: MemristorModel,
) -> OptimizeResult<OptimizeResults<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let mut optimizer = MemristiveOptimizer::new(
        initial_params.to_owned(),
        learning_rate,
        0.9,
        memristor_params,
        model,
    );

    optimizer.optimize(objective, max_nit)
}

/// Memristive Neural Network Optimizer for ML problems
#[allow(dead_code)]
pub fn memristive_neural_optimizer<F>(
    objective: F,
    initial_weights: &ArrayView2<f64>,
    learning_rate: f64,
    max_nit: usize,
) -> Result<Array2<f64>>
where
    F: Fn(&ArrayView2<f64>) -> f64,
{
    let (rows, cols) = initial_weights.dim();
    let params = MemristorParameters::default();
    let mut crossbar = MemristiveCrossbar::new(rows, cols, params, MemristorModel::TeamModel);

    // Initialize crossbar with weights
    for i in 0..rows {
        for j in 0..cols {
            let _target_conductance = initial_weights[[i, j]].abs() * 1e-3; // Scale to conductance
            let voltage = if initial_weights[[i, j]] > 0.0 {
                1.0
            } else {
                -1.0
            };
            crossbar.memristors[i][j].update(voltage, 1e-3);
        }
    }

    for _iter in 0..max_nit {
        // Get current weights from crossbar
        let current_weights = crossbar.get_conductance_matrix();
        let objective_value = objective(&current_weights.view());

        // Compute weight gradients (simplified)
        let mut weight_gradients = Array2::zeros((rows, cols));
        let h = 1e-6;

        for i in 0..rows {
            for j in 0..cols {
                let mut perturbed_weights = current_weights.clone();
                perturbed_weights[[i, j]] += h;
                let f_plus = objective(&perturbed_weights.view());
                weight_gradients[[i, j]] = (f_plus - objective_value) / h;
            }
        }

        // Update crossbar based on gradients
        for i in 0..rows {
            let row_input = weight_gradients.row(i).to_owned();
            let target = Array1::zeros(cols); // Target is zero update
            crossbar
                .update(&row_input.view(), &target.view(), learning_rate)
                .ok();
        }
    }

    Ok(crossbar.get_conductance_matrix())
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_memristor_models() {
        let params = MemristorParameters::default();
        let mut memristor = Memristor::new(params, MemristorModel::NonlinearIonicDrift);

        let initial_resistance = memristor.resistance;
        memristor.update(1.0, 1e-3);

        // Resistance should change with applied voltage
        assert!(memristor.resistance != initial_resistance);
    }

    #[test]
    fn test_crossbar_operations() {
        let params = MemristorParameters::default();
        let mut crossbar = MemristiveCrossbar::new(3, 3, params, MemristorModel::LinearIonicDrift);

        let input = Array1::from(vec![1.0, 0.5, 0.0]);
        let output = crossbar.multiply(&input.view());

        assert_eq!(output.len(), 3);
        assert!(output.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_memristive_optimization() {
        let objective = |x: &ArrayView1<f64>| x[0].powi(2) + x[1].powi(2);
        let initial = Array1::from(vec![1.0, 1.0]);

        let result = memristive_gradient_descent(objective, &initial.view(), 0.1, 100);
        assert!(result.is_ok());

        let final_params = result.unwrap();
        let final_obj = objective(&final_params.view());
        let initial_obj = objective(&initial.view());

        // Should improve from initial solution
        assert!(final_obj < initial_obj);
    }

    #[test]
    fn test_crossbar_with_faults() {
        let params = MemristorParameters::default();
        // Reduce size from 10x10 to 3x3 for faster test execution
        let mut crossbar = MemristiveCrossbar::new(3, 3, params, MemristorModel::TeamModel);

        // Should handle faults gracefully
        let _faulty_count = crossbar.fault_map.iter().filter(|&&x| x).count();
        // Some devices may be faulty - this is expected behavior

        let input = Array1::ones(3);
        let output = crossbar.multiply(&input.view());
        assert!(output.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_temperature_effects() {
        let params = MemristorParameters::default();
        let mut memristor = Memristor::new(params, MemristorModel::NonlinearIonicDrift);

        let resistance_at_300k = memristor.resistance;

        memristor.set_temperature(350.0); // Higher temperature
        memristor.update(1.0, 1e-3);
        let resistance_at_350k = memristor.resistance;

        // Temperature should affect resistance evolution
        assert!(resistance_at_350k != resistance_at_300k);
    }
}